{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Basic_DCA.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPW2XlqF80WYlJdDK6lorTr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/Basic_DCA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCKjhGDS1gVV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqpe48JA8YvX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from plotly.offline import iplot\n",
        "\n",
        "import plotly.graph_objects as go"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuHKdY3z1pJ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('DCA.txt',names=['A','B','C','D'],sep=' ')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQrfvAU-12fU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.columns = ['Month', 'Year', 'Monthly Production(bbls)', 'Cumm Production(bbls)']"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juQANfL_14aI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df['Month'] = np.int64(df['Month'])"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svrCibbN3A02",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Month'] = np.array([1,2,3,4,5,6,7,8,9,10,11,12])"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPqJHDFU3MNN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "coeffs = np.polyfit(df['Month'],df['Monthly Production(bbls)'] , deg=2)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odE9c5ll31A8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "93e71e56-e767-4926-d90a-66e6d938f1c5"
      },
      "source": [
        "coeffs\n",
        "\n",
        "np.polyfit()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  136.05669331, -3314.60064935, 30932.25      ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DKxsyZzQ5Znr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "q_p = coeffs[0]*(df['Month']**2) + coeffs[1]*df['Month'] + coeffs[2]"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6thG66e7qlJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "b8309bdd-33d4-4c7e-91d8-16058a8392f9"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Monthly Production(bbls)</th>\n",
              "      <th>Cumm Production(bbls)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1952</td>\n",
              "      <td>29500</td>\n",
              "      <td>29500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1952</td>\n",
              "      <td>23606</td>\n",
              "      <td>53106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1952</td>\n",
              "      <td>21301</td>\n",
              "      <td>74407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1952</td>\n",
              "      <td>19318</td>\n",
              "      <td>93725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1952</td>\n",
              "      <td>17599</td>\n",
              "      <td>111324</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Month  Year  Monthly Production(bbls)  Cumm Production(bbls)\n",
              "0      1  1952                     29500                  29500\n",
              "1      2  1952                     23606                  53106\n",
              "2      3  1952                     21301                  74407\n",
              "3      4  1952                     19318                  93725\n",
              "4      5  1952                     17599                 111324"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QaXYwXQ15imV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "7b8e65b4-bd3b-4d4c-95e9-b4e5599165b5"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.style.use('default')\n",
        "\n",
        "plt.plot(df['Month'], df['Monthly Production(bbls)'] , linewidth='5' ,label = 'Actual Production Data')\n",
        "\n",
        "plt.plot(df['Month'], q_p , linewidth='5',label = 'Model Predictions')\n",
        "\n",
        "plt.plot(df['Month'], df['Cumm Production(bbls)']/10, linewidth='5', label = 'Cumm. Production(bbls)')\n",
        "\n",
        "\n",
        "\n",
        "plt.xlabel('Time (Months)')\n",
        "plt.ylabel('Monthly Prod. (bbls)')\n",
        "\n",
        "\n",
        "plt.legend(loc='best')\n",
        "plt.title('DCA: Model good enough to predict future Q(bbl/month)')\n",
        "plt.grid()\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QERFBZmYmI0aM0FmunWzlYdu2baNBgwY0bdpUXWZjY8PQoUOJi4sjJiZGTefk5KRTB6ampowZM4aUlBT279+vpjMxMWH48OFqOmNj41z3nZu1a9fSrFkzHB0ddcqibdu2ZGVl8euvv+qk7927N46OjupnbZ1dvHgRMLy+CmLHjh0ABpWv1qOTJTRr1ozbt2+r10NBGVof+XF2dtb5dV7bxez48eN6k9y88cYbGBsbq5/Dw8NJSEhQr0Ptn7GxMQ0bNlTPWW35BwYGYm9vr27frl07vL29840vOzubTZs2ERAQoDM+VetJXx3xIgkMDFTH1kLO5Brnzp2jb9++3L59Wy3X1NRU2rRpw6+//qrTdfdRycnJj723atdr78m3b98G0LmOHlaQ82To0KE6vQqGDx+OiYnJY6+zrVu3Ym9vr3Mf0hoyZIj6/w4ODnh6emJtba0zdbynpycODg7qdQ+G39u0goODMTMzUz8/ei8xREHy0N7jhHjRSJdFIV5SKSkplClTBsj5xxzIdzr8R61cuRJra2sqVaqk9se3sLDAzc2NVatWFcog8L59+/LFF19w4sQJVq9eTZ8+fXL9wqd9R4+np6feOi8vL3bu3ElqairJycncv38fDw8PvXSenp46X1DOnTuHoii5pgUKNAPcwzFWrlxZZ3mJEiX0vnTFx8frjTXRHot2ffXq1YmPj8fDw0NvwoGH02n/W65cOb2uPLmVV27OnTvHyZMnKV26dK7rtRMyaD3a7VV7fNpxK4bWV0EmqYiPj8fIyEjtGqX1aHkbGqf2migIQ+sjP5UrV9Y7x6tUqQLkjNFzcnJSlz96rOfOnQNyxonmRntM2jjyug6OHTuWZ3y3bt0iKSmJ6tWrP+5Q/rPyKtf8uvgmJibm2XiytbV97Jd87b1Xe0/WUhQl1/QFOU8erWcbGxvKlSv32Bkwt27dSvv27TEx0f0qaGFhoXcvsLe3p3z58nox2dvb64xXM/TepvW4e4khCpKHoigvxY8K4uUjDTIhXkL//PMPiYmJ6pfVypUrY2JiwqlTpwzaXlEUfvjhB1JTU3P9Rf3mzZukpKQ89UxVDRs2xN3dnbFjxxIbG0vfvn2fKr+CyM7ORqPRsH37dp2nEFrFaRau7Oxs2rVrx4QJE3Jdr/0iqJVbeUHeXy7zk9eXo9zeiVdQhRlnUXj4KQ6gPqVZsWKFzhdyrUe/WP8X5Ff/edXf0+aRV7l+8skn1K5dO9e88rsfeHt7Ex0dzaVLl/Ico6t9DUmlSpUAKFmyJFCwhkdhunfvHvv27WPRokV66/Iq92dxPRVGngXJ4+7du3n+CCdEUfrv3b2FEI+1YsUKALWbnpWVFa1bt2bPnj1cvnwZFxeXfLffv38///zzD9OnT9cbqH/37l2GDh3Kpk2b6N+//1PH+vrrrzNjxgy8vLzy/DKkfWnq2bNn9dadOXOGUqVKYW1tjYWFBZaWluov3g97dFt3d3cURaFixYp6DY4noY3x/PnzOr/A3759W+9Ll6ura57H8nBerq6unDx5kuzsbJ2nMrmli4iI0Gsk57aP3Li7u5OSkpLny7oLytD6gpxfs3ObDfLRp02urq5kZ2cTGxur84Uqt9nUnlZeX/ANrY/8nD9/Xu9X+r///hvgsROdaCfCKFOmTL51pY3DkOvgUaVLl8bOzi7XV2A8rLCeMuRX/9rGy+P2Z2geedGWq52d3RNdAwEBAaxevZrvv/8+1/e5JSUl8fPPP1O3bl01nqpVqwIQGxuba54FOU/OnTtHq1at1M8pKSlcu3Yt35c979mzh7S0NPz9/Q07SAMZem8riMI61zIzM7l8+TKvvvpqoeQnRGGSMWRCvGT27NnDRx99RMWKFenXr5+6fOrUqSiKwoABA0hJSdHb7ujRo+r03druiuPHj6dnz546f2+88QYeHh46L4ku6LT3DxsyZAhTp07N94Wd5cqVo3bt2ixfvlzni9eff/7Jrl271C8exsbG+Pn5sWnTJi5duqSmO336tN64t+7du2NsbMy0adP0fklVFEUd42GoNm3aYGJioveL84IFC/TSduzYkSNHjnDo0CF1WWpqKosXL8bNzU19KtmxY0euX7/OmjVr1HSZmZl8+eWX2NjY0KJFCzVdZmamzr6zsrL48ssvDYq9V69eHDp0SK+MIGfq+MzMTIPy0TK0viDny3BiYqLOi8yvXbumN0W79seFhQsX6iw39BgLwtramsTERL3lhtZHfq5evapzbElJSXz//ffUrl0716deD/Pz88POzo5Zs2aRkZGht/7WrVuAbvk/fBzh4eF6Y3geZWRkRNeuXfnll1+IiorSW6+9VrQN6oK8WiE37u7u/PHHH6Snp6vLtmzZovfqifz2Z2geealXrx7u7u58+umnud4bteWalx49elCtWjVmz56tV2bZ2dkMHz6cu3fv8sEHH6jLX3nlFVxcXHItYyjYebJ48WKd82HRokVkZmbm29jatm0bPj4+uc4++zQMvbcVRF7XY0HFxMTw4MGDJ56dU4hnSZ6QCfEftn37ds6cOUNmZiY3btxgz549hIeH4+rqyubNm3Ve+Nq4cWO++uorRowYQdWqVRkwYAAeHh4kJyezb98+Nm/ezIwZM0hLS2P9+vW0a9cuzxfGvvrqq8yfP5+bN29SpkyZJ5r2XsvV1TXXd0g96pNPPsHf3x9fX18GDx6sTqNub2+vs/20adPYsWMHzZo1Y8SIEeoX5mrVqul86Xd3d2fGjBlMmjSJuLg4unbtiq2tLbGxsWzcuJGhQ4fy7rvvGnwcZcuW5a233mLu3Lm8+uqrdOjQgRMnTrB9+3ZKlSql8yvvxIkT+eGHH/D392fMmDGUKFGC5cuXExsby/r169WnL0OHDuWbb74hKCiIo0eP4ubmxrp16zh48CDz5s1TJwoICAigSZMmTJw4kbi4OPWdRYZ+iRk/fjybN2+mc+fOBAUFUa9ePVJTUzl16hTr1q0jLi6OUqVKGVwWYHh99enTh/fee49u3boxZswYdRr3KlWq6Ix1qlevHj169GDevHncvn1bnfZe+9SgMMeF1KtXjzVr1jBu3Djq16+PjY0NAQEBBtdHfqpUqcLgwYOJjIykbNmyfPfdd9y4cYNly5Y9dls7OzsWLVrEgAEDqFu3Ln369KF06dJcunSJrVu30qRJE/UHgI8//phOnTrRtGlTBg0axJ07d9TrILdGx8NmzZrFrl27aNGiBUOHDsXLy4tr166xdu1aDhw4gIODA7Vr18bY2Jg5c+aQmJiIubk5rVu31hsj9ThDhgxh3bp1dOjQgV69enHhwgVWrlypNy2+u7s7Dg4OfP3119ja2mJtbU3Dhg2pWLGiwXnkxcjIiCVLluDv70+1atUIDg7mlVde4cqVK+zduxc7Ozt1avvcmJqasn79elq3bk3Tpk0JDg7Gx8eHhIQEVq9ezbFjx3j//ffp3r27znZdunRh48aNuY5rKsh5kp6eTps2bejVqxdnz55l4cKFNG3aNN8nQdu2bSM4ONig8ikIQ+9tBZHX9VhQ4eHhWFlZ6b02QIgXwvOe1lEI8fS00x1r/8zMzBQnJyelXbt2yvz58/Odfvvo0aNK3759FWdnZ8XU1FRxdHRU2rRpoyxfvlzJyspS1q9frwDK0qVL88xj3759CqDMnz9fUZQnm/bekON7dIrs3bt3K02aNFEsLS0VOzs7JSAgQImJidHbfv/+/Uq9evUUMzMzpVKlSsrXX3+tN4261vr165WmTZsq1tbWirW1tVK1alVl5MiRytmzZ9U0hkx7ryg5U81PnjxZcXJyUiwtLZXWrVsrp0+fVkqWLKkMGzZMJ+2FCxeUnj17Kg4ODoqFhYXSoEEDZcuWLXp53rhxQwkODlZKlSqlmJmZKTVq1Mh16u/bt28rAwYMUOzs7BR7e3tlwIAByvHjxw2a9l5RcqamnjRpklK5cmXFzMxMKVWqlNK4cWPl008/VafU1k57/8knn+htzyPTXyuK4fW1a9cupXr16oqZmZni6emprFy5Mtf6Sk1NVUaOHKmUKFFCsbGxUbp27aqcPXtWAZTZs2er6bTb3rp1S2d77XkVGxubb1mkpKQoffv2VRwcHPSmiTe0PnKjPfd37typ1KxZUzE3N1eqVq2qrF27Ntc483qtxd69exU/Pz/F3t5esbCwUNzd3ZWgoCAlKipKJ9369esVLy8vxdzcXPH29lY2bNiQ67mcW93Fx8crAwcOVEqXLq2Ym5srlSpVUkaOHKkzvfi3336rVKpUSX2lRH7Xf37HNHfuXOWVV15RzM3NlSZNmihRUVG5TtP+888/K97e3oqJiYneeW1IHtpp0h8tb63jx48r3bt3V0qWLKmYm5srrq6uSq9evZSIiIg8j+tht27dUt555x31GtLen/O6lx47dkxvinhFKfh5sn//fmXo0KGKo6OjYmNjo/Tr10/ndROKojvt/Z9//qkAypEjR/RiCgwMVKytrfWWt2jRQqlWrZre8tzu54bc2/KqC+095uG6zet6LEgeiqIoDRs2VPr37693DEK8CDSK8h8Z3SyEEP9BCQkJODo6MmPGDJ0uS6JwREdHU6dOHVauXKnTRfdF5ObmRvXq1dmyZUtRhyKeg1OnTtGsWTNcXFw4cOCAzisItNq0aYOzs7M67vd5CA0N5bPPPuPatWvFZsbB6Oho6taty7Fjx/IcqyxEUZIxZEIIUUju37+vt2zevHkABe7KKfTlVb5GRkY0b968CCISIm81atTg559/5ty5c3Tt2lVnjJvWrFmzWLNmjUGvTCgsbm5ufP7558WmMQYwe/ZsevbsKY0x8cKSJ2RCCFFIwsLCCAsLo2PHjtjY2HDgwAF++OEH2rdvn+uEGaJgpk2bxtGjR2nVqhUmJiZs376d7du3q2O7XnTyhEwIIURuZFIPIYQoJDVr1sTExITQ0FCSkpLUiT5mzJhR1KG9FBo3bkx4eDgfffQRKSkpVKhQgZCQEOkKKoQQ4j9NnpAJIYQQQgghRBGRMWRCCCGEEEIIUUSkQSaEEEIIIYQQRUTGkBWS7Oxsrl69iq2tbbGauUgIIYQQQgihS1EUkpOTcXZ2fuxL0aVBVkiuXr2Ki4tLUYchhBBCCCGEeEFcvnyZ8uXL55tGGmSFxNbWFsgpdDs7uyKOpnjLyMhg165dtG/fHlNT06IORxQBOQeKN6n/4k3qv3iT+i/eXqT6T0pKwsXFRW0j5EcaZIVE203Rzs5OGmRFLCMjAysrK+zs7Ir8YhRFQ86B4k3qv3iT+i/epP6Ltxex/g0ZyiSTegghhBBCCCFEEZEGmRBCCCGEEEIUEWmQCSGEEEIIIUQRkTFkQgghhBAvKUVRyMzMJCsrq6hDeS4yMjIwMTHhwYMHxeaYxf88z/o3NjbGxMSkUF53VaQNskWLFrFo0SLi4uIAqFatGlOmTMHf3x+ABw8e8M477/Djjz+SlpaGn58fCxcupGzZsmoely5dYvjw4ezduxcbGxsCAwP5+OOPMTH536Ht27ePcePG8ddff+Hi4sKHH35IUFCQTixfffUVn3zyCdevX6dWrVp8+eWXNGjQ4JmXgRBCCCHEs5Cens61a9e4d+9eUYfy3CiKgpOTE5cvX5b3whZDz7v+raysKFeuHGZmZk+VT5E2yMqXL8/s2bPx8PBAURSWL19Oly5dOH78ONWqVePtt99m69atrF27Fnt7e0aNGkX37t05ePAgAFlZWXTq1AknJyd+//13rl27xsCBAzE1NWXWrFkAxMbG0qlTJ4YNG8aqVauIiIhgyJAhlCtXDj8/PwDWrFnDuHHj+Prrr2nYsCHz5s3Dz8+Ps2fPUqZMmSIrHyGEEEKIJ5GdnU1sbCzGxsY4OztjZmZWLBoo2dnZpKSkYGNj89iX8YqXz/Oqf0VRSE9P59atW8TGxuLh4fF0+1NeMI6OjsqSJUuUhIQExdTUVFm7dq267vTp0wqgHDp0SFEURdm2bZtiZGSkXL9+XU2zaNEixc7OTklLS1MURVEmTJigVKtWTWcfvXv3Vvz8/NTPDRo0UEaOHKl+zsrKUpydnZWPP/7Y4LgTExMVQElMTCzYAYtCl56ermzatElJT08v6lBEEZFzoHiT+t1WdvoAACAASURBVC/epP5z3L9/X4mJiVFSU1OLOpTnKisrS7l7966SlZVV1KGIIvC86z81NVWJiYlR7t+/r7euIG2DF2YMWVZWFmvXriU1NRVfX1+OHj1KRkYGbdu2VdNUrVqVChUqcOjQIRo1asShQ4eoUaOGThdGPz8/hg8fzl9//UWdOnU4dOiQTh7aNGPHjgVyHucfPXqUSZMmqeuNjIxo27Ythw4dyjPetLQ00tLS1M9JSUlATt/VjIyMpysM8VS05S/1UHzJOVC8Sf0Xb1L/OTIyMlAUBch5alBcaI9ZUZRiddwiR1HUv6IoZGRkYGxsrLO8IPegIm+QnTp1Cl9fXx48eICNjQ0bN27E29ub6OhozMzMcHBw0ElftmxZrl+/DsD169d1GmPa9dp1+aVJSkri/v373L17l6ysrFzTnDlzJs+4P/74Y6ZNm6a3fNeuXVhZWRl49OJZCg8PL+oQRBGTc6B4k/ov3op7/ZuYmODk5ERKSgrp6elFHc5zl5ycXNQhiCL0vOo/PT2d+/fv8+uvv5KZmamzriBjN4u8Qebp6Ul0dDSJiYmsW7eOwMBA9u/fX9RhPdakSZMYN26c+jkpKQkXFxfat2+PnZ1dEUaW4+cT1/CtVIIytuZFHcpzl5GRQXh4OO3atXth3tIuni85B4o3qf/iTeo/x4MHD7h8+TI2NjZYWFgUdTjPjaIoJCcnY2trWyzGzAldz7v+Hzx4gKWlJc2bN9e7zrS95wxR5A0yMzMzKleuDEC9evWIjIxk/vz59O7dm/T0dBISEnSekt24cQMnJycAnJycOHLkiE5+N27cUNdp/6td9nAaOzs7LC0tMTY2xtjYONc02jxyY25ujrm5fmPH1NS0SP8ByMzK5uPtZ1h6IJZaLg6sGdoIC1Pjx2/4EirquhBFT86B4k3qv3gr7vWflZWFRqPByMioWE1uoe2mpj32Z0Gj0bBx40a6du36TPIviKCgIBISEti0adMz3Y+bmxtjx45Vh/y8qJ5H/T/MyMgIjUaT6/2mIPefF+4Kzc7OJi0tjXr16mFqakpERIS67uzZs1y6dAlfX18AfH19OXXqFDdv3lTThIeHY2dnh7e3t5rm4Ty0abR5mJmZUa9ePZ002dnZREREqGn+KxLvZzBoeRRLD8QCcOJyApM2nFL70wohhBBC/FccOnQIY2NjOnXqVOBtK1WqxLx5855BVI8XFBSERqNBo9GoDx6mT5+u16XtRRQWFqY3XAggMjKSoUOHPtN979u3Ty03IyMj7O3tqVOnDhMmTODatWsFzk+j0TzzhmphKdInZJMmTcLf358KFSqQnJzM6tWr2bdvHzt37sTe3p7Bgwczbtw4SpQogZ2dHaNHj8bX15dGjRoB0L59e7y9vRkwYAChoaFcv36dDz/8kJEjR6pPr4YNG8aCBQuYMGECgwYNYs+ePfz0009s3bpVjWPcuHEEBgbi4+NDgwYNmDdvHqmpqQQHBxdJuTyJi7dSGLI8iov/puos33j8Cp5Otgxr4V5EkQkhhBCiqFWbsoOMrKL/gdbUWMNf0zsYlHbp0qWMHj2apUuXcvXqVZydnZ9xdIWnQ4cOLFu2jLS0NLZt28bIkSMxNTXVmUROKz09/anfY/WslS5d+rnt6+zZs9jZ2ZGUlMSxY8cIDQ1l6dKl7Nu3jxo1ajy3OJ6nIn1CdvPmTQYOHIinpydt2rQhMjKSnTt30q5dOwA+//xzOnfuTI8ePWjevDlOTk5s2LBB3d7Y2JgtW7ZgbGyMr68v/fv3Z+DAgUyfPl1NU7FiRbZu3Up4eDi1atVi7ty5LFmyRH0HGUDv3r359NNPmTJlCrVr1yY6OpodO3boTfTxooq+nECXrw7qNca05uw4Q8TpG7muE0IIIcTLLyNLIT0ru8j/DG0UpqSksGbNGoYPH06nTp0ICwvTS/PLL79Qv359LCwsKFWqFN26dQOgc+fOxMfH8/bbb6tPXABCQkKoXbu2Th7z5s3Dzc1N/RwZGUm7du0oVaoU9vb2tGjRgmPHjhW4vM3NzXFycsLV1ZXhw4fTtm1bNm/eDOQ8QevatSszZ87E2dkZT09PIGeiu9atW2NpaUnJkiUZOnQoKSkpap5ZWVmMGzcOBwcHSpYsyYQJE/R6Qbm5uek9GaxduzYhISHq54SEBN58803Kli2LhYUF1atXZ8uWLezbt4/g4GASExPVctNu92i+ly5dokuXLtjY2GBnZ0evXr10hv9oy3rFihW4ublhb29Pnz59DJpso0yZMjg5OVGlShX69OnDwYMHKV26NMOHD1fTPK6eKlWqBEC3bt3QaDRqHV+4cIEuXbpQtmxZbGxsqF+/Prt3735sTM9akTbIli5dSlxcHGlpady8eZPdu3erjTEACwsLvvrqK+7cuUNqaiobNmzQG9fl6urKtm3buHfvHrdu3eLTTz/FxET3wV/Lli05fvw4aWlpXLhwgaCgIL1YRo0aRXx8PGlpaRw+fJiGDRs+k2N+FiqVtqasXd4DdhUF3voxmr9vyIxDQgghhHjx/fTTT1StWhVPT0/69+/Pd999p9P42Lp1K926daNjx44cP36ciIgIGjRoAMCKFSsoX74806dP59q1awXq7pacnExgYCAHDhzgjz/+wMPDg44dOz71rH2WlpY6s11GRERw9uxZwsPD2bJlC6mpqfj5+eHo6EhkZCRr165l9+7djBo1St1m7ty5hIWF8d1333HgwAHu3LnDxo0bCxRHdnY2/v7+HDx4kJUrVxITE8Ps2bMxNjamcePGzJs3Dzs7O7Xc3n333Vzz6NKlC3fu3GH//v2Eh4dz8eJFevfurZPuwoULbNq0iS1btrBlyxb279/P7NmzC1hyOWU3bNgwDh48qA5Telw9HT58GIBly5Zx7do1IiMjgZyGfseOHYmIiOD48eN06NCBgIAALl26VOC4ClORT+ohnp6dhSlLBvrQ5auDJN7P/Z0HKWmZDFkexc8jm+Bo/WI/FhdCCCFE8bZ06VL69+8P5HT/S0xMZP/+/bRs2RKAmTNn0qdPH51XENWqVYvs7GwcHR0xNjbG1tY23wnactO6dWudz4sXL8bBwYH9+/fTuXPnAh+HoihERESwc+dORo8erS63trZmyZIlalfFb7/9lgcPHvD9999jbW0NwIIFCwgICGDOnDmULVuWefPmMWnSJLp37w7A119/zc6dOwsUz+7duzly5AinT5+mSpUqwP+eJgHY29uj0WjyLbeIiAhOnTpFbGwsLi4uAHz//fdUq1aNyMhI6tevD+Q03MLCwrC1tQVgwIABREREMHPmzALFDDnvIgaIi4ujTJky+dZT8+bN1S6WDg4OOsdSq1YtatWqpX7+6KOP2LhxI5s3b9Zp/D5vL9ykHuLJuJWyZmG/uhgb5T3F56U79xix6hgZWfKiRCGEEEK8mM6ePcuRI0d4/fXXgZx3qvXu3ZulS5eqaaKjo2nTpk2h7/vGjRu88cYbeHh4YG9vj52dHSkpKQV+grJlyxb1lQP+/v707t1bp9tgjRo1dMaNnT59mlq1aqmNMYAmTZqQnZ3N2bNnSUxM5Nq1azo9uExMTPDx8SlQXNHR0ZQvX15tjD2J06dP4+LiojbGALy9vXFwcOD06dPqMjc3N7UxBlCuXDmdifgKQvt0VNv9NK96unz5cr75pKSk8O677+Ll5YWDgwM2NjacPn1anpCJwtOkcimmdPZm6ua/8kxz6OJtpv8Sw0ddqz/HyIQQQgghDLN06VIyMzN1JvFQFAVzc3MWLFiAvb09lpaWBc7XyMhIb8xVRoZuz6LAwEBu377N/PnzcXV1xdzcHF9f3wK/XLtVq1YsWrQIMzMznJ2d9YbTPNzwKkyPO8YnKbcn9ei07xqNRp2WvqC0DT3tWLAnrad3332X8PBwPv30UypXroylpSU9e/Ys8penyxOyl8xAX1deb1Ah3zQr/ohn5R/xzykiIYQQQhQ1U2MNZsZGRf5napz/y3ozMzP5/vvvmTt3LtHR0erfiRMncHZ25ocffgCgZs2aeq81epiZmRlZWVk6y0qXLs3169d1GizR0dE6aQ4ePMiYMWPo2LEj1apVw9zcnH///begxY21tTWVK1emQoUKeo2x3Hh5eXHixAlSU/83QdvBgwcxMjLC09MTe3t7ypUrp46NgpyyOnr0qN4xPjxmLikpidjYWPVzzZo1+eeff/j7779zjSO3csst1suXL+s8jYqJiSEhIUF97VRhun//PosXL9bpimhIPZmamuody8GDBwkKCqJbt27UqFEDJycn4uLiCj3mgpInZC8ZjUbDtFerceFWCkdi7+SZLmTzX7iXtsHXveRzjE4IIYQQRcHQqeaL2pYtW7h79y6DBw/G3t5eZ12PHj1YunQpw4YNY+rUqbRp0wZ3d3f69OlDZmYm27ZtY/z48UDOpG+//vorffr0wdzcnFKlStGyZUtu3bpFaGgoPXv2ZMeOHWzfvh07Ozt1Hx4eHqxYsQIfHx+SkpIYP378c3mq1K9fP6ZOnUpgYCAhISHcunWL0aNHM2DAAHXW77feeovZs2fj4eFB1apV+eyzz0hISNDJp3Xr1oSFhREQEICDgwNTpkzB2NhYXd+iRQuaN29Ojx49+Oyzz6hcuTJnzpxBo9HQoUMH3NzcSElJISIiglq1amFlZYWVlZXOPtq2bUuNGjXo168f8+bNIzMzkxEjRtCiRYsCd6HMzc2bN3nw4AHJyckcPXqU0NBQ/v33X52Z1g2pJzc3NyIiImjSpAnm5uY4Ojri4eHBhg0bCAgIQKPRMHny5Cd+aleY5AnZS8jMxIiv+9ejvGPeN5DMbIXhq45y6fa95xiZEEIIIUTeli5dStu2bfUaY5DTIIuKiuLkyZO0bNmStWvXsnnzZmrXrk3r1q05cuSImnbatGnExcXh7u6uPlXx8vJi4cKFfPXVV9SqVYsjR47ozSK4dOlS7t69S926dRkwYABjxoyhTJkyz/agASsrK3bu3MmdO3eoX78+PXv2pE2bNixYsEBN88477zBgwAACAwPx9fXF1tZWnepfa9KkSbRo0YLOnTvTqVMnunbtiru77rto169fT/369Xn99dfx9vZmwoQJ6pOkxo0bM2zYMHr37k3p0qUJDQ3Vi1Wj0fDzzz/j6OhI8+bNadu2LZUqVWLNmjWFUhaenp44OztTr149Zs+eTdu2bfnzzz91nr4ZUk9z584lPDwcFxcX6tSpA8Bnn32Go6MjjRs3JiAgAD8/P+rWrVsocT8NjfJoR1PxRJKSkrC3tycxMVHnl5aidOZ6Ej0W/k5qet6PnquUtWH98MbYWpjmmea/JiMjg23bttGxY0e9/suieJBzoHiT+i/epP5zPHjwgNjYWCpWrIiFRd6vxnnZZGdnk5SUhJ2dHUZG8tyhuHne9Z/fdVaQtoGcqS+xqk52fN67Npp8umv/fSOFt9dEk5Ut7XIhhBBCCCGeN2mQveTaV3Pi3fae+abZffomn+46+5wiEkIIIYQQQmhJg6wYGNHSnS61nfNNs2jfBTYdv/KcIhJCCCGEEEKANMiKBY1Gw5weNalVXn+A7MMmrD9J9OWEfNMIIYQQQgghCo80yIoJC1NjvhngQxlb8zzTpGdmM/T7KK4nPniOkQkhhBBCCFF8SYOsGHGyt2DxQB/MTPKu9pvJaQxdEcWDjPxfCiiEEEIIIYR4etIgK2ZquzjwSc+a+aY5+U8iE9adRN6IIIQQQgghxLMlDbJiqEvtVxje0j3fNJtPXGXhvgvPKSIhhBBCCCGKJ2mQFVPj23vS1iv/N89/uuss4TE3nlNEQgghhBBCFD/SICumjIw0zOtThyplbfJMoygw9sfjnLme9BwjE0IIIYR4tvbt24dGoyEhwfDZpd3c3Jg3b94zjMpwISEh1K5dW/0cFBRE165dnyrPwshDPBlpkL1ssjLh7505ranHsDE3YcnA+jhameaZJjU9iyHLo7iTml6YUQohhBBC5CooKAiNRsOwYcP01o0cORKNRkNQUNDzD+wxQkJC0Gg0aDQaTExMcHNz4+233yYlJeWZ73v+/PmEhYUZlDYuLg6NRkN0dPQT5yEKl0lRByAKWeQS2PEeVGwBHT+B0p75Jq9Q0oqF/eoxYOlhMrNzb8T9c/c+w1ceZcXghvnO0CiEEEKIF9SsVyDrBfhx1dgM3r/y2GQuLi78+OOPfP7551haWgLw4MEDVq9eTYUKFZ51lE+sWrVq7N69m8zMTA4ePMigQYO4d+8e33zzjV7a9PR0zMzMCmW/9vb5v2v2eeUhnox8u36ZpNyEvTNz/j92PyxqDLsmQ1pyvpv5upck5NVq+aY5HHuHkF/+kpkXhRBCiP+irPQX588AdevWxcXFhQ0bNqjLNmzYQIUKFahTp45O2rS0NMaMGUOZMmWwsrKiQ4cOREZG6qTZtm0bVapUwdLSklatWhEXF6e3zwMHDtCsWTMsLS1xcXFhzJgxpKamFqiYTUxMcHJyonz58vTu3Zt+/fqxefNm4H/dDJcsWULFihWxsLAAICEhgSFDhlC6dGns7Oxo3bo1J06c0Ml39uzZlC1bFltbWwYPHsyDB7rvjH20u2F2djahoaFUrlwZc3NzKlSowMyZOd8RK1asCECdOnXQaDS0bNky1zweLlcLCwuaNm2qU67abp8RERH4+PhgZWVF48aNOXv2rJrmxIkTtGrVCltbW+zs7KhXrx5RUVEFKtPiQBpkL5PwqZD20Hiv7Ez4/QtYUB9Orcu3G2P/Rq4MaOSab/arD19ixR/xhRWtEEIIIUSeBg0axLJly9TP3333HcHBwXrpJkyYwPr161m+fDlRUVFUqlQJf39/7ty5A8Dly5fp3r07AQEBREdHM2TIECZOnKiTx4ULF+jQoQM9evTg5MmTrFmzhgMHDjBq1KinOgZLS0vS0//XCD1//jzr169nw4YNapfB1157jZs3b7J9+3aOHj1K3bp1adOmjRr/Tz/9REhICLNmzSIqKopy5cqxcOHCfPc7adIkZs+ezeTJk4mJiWH16tWULVsWgCNHjgCwe/durl27ptPofdjD5Xrs2DEqV66Mn5+fGpfWBx98wNy5c4mKisLExIRBgwap6/r160f58uWJjIzk6NGjTJw4EVPTvIfKFFfSIHtZXPoDTqzOfV3yNVg/GJYHwM3TeWYxJcAb30ol893NtF9iOHj+36eJVAghhBDisfr378+BAweIj48nPj6egwcP0r9/f500qampLFq0iE8++QR/f3+8vb2ZP38+lpaWLF26FIBFixbh7u7O3Llz8fT0pF+/fnpj0D7++GP69evH2LFj8fDwoHHjxnzxxRd8//33ek+jDHX06FFWr15N69at1WXp6el8//331KlTh5o1a3LgwAGOHDnC2rVr8fHxwcPDg08//RQHBwfWrVsHwLx58xg8eDCDBw/G09OTGTNm4O3tned+k5OTmT9/PqGhoQQGBuLu7k7Tpk0ZMmQIAKVLlwagZMmSODk5UaJECb08civXb7/9VqdctWbOnEmLFi3w9vZm4sSJ/P7772qZXbp0ibZt21K1alU8PDx47bXXqFWr1hOV58tMGmQvg6xM2Pru49PF/QZfN4WdH+TajdHU2IiF/eriWtIq711lK4xYdYy4fwv2CF8IIYQQoiBKly5Np06dCAsLY9myZXTq1IlSpUrppLlw4QIZGRk0adJEXWZqakr9+vU5fTrnR+jTp0/TsGFDne18fX11Pp84cYKwsDBsbGzUPz8/P7Kzs4mNjTU45lOnTmFjY4OlpSUNGjTA19eXBQsWqOtdXV3VBpF2vykpKZQsWVJn37GxsVy4cMHg+B92+vRp0tLSaNOmjcFxPyqvcm3QoIFarlo1a9ZU/79cuXIA3Lx5E4Bx48YxZMgQ2rZty+zZs9VjErpkUo+XwZ2LkHLdsLTZmXBoQU4XxvYzoEZP0GjU1Y7WZiwZ6EO3hb+TkpaZaxaJ9zMY8n0UG0Y0xs5CHjsLIYQQ4tkYNGiQ2m3wq6++emb7SUlJ4c0332TMmDF66woyiYinpyebN2/GxMQEZ2dnvUk7rK2t9fZbrlw59u3bp5eXg4ODwft9mHYSlOfl4S6Imv//TpmdnQ3kjJvr27cvW7duZfv27UydOpUff/yRbt26PdcYX3TyhOxlULoKjIqChsNAY2CVplyHDUMgrDPciNFZ5VHWli9er/1wO03P+ZspvPXDcbLymJlRCCGEEC8QY7MX568AOnToQHp6OhkZGfj5+emtd3d3x8zMjIMHD6rLMjIyiIqKUrv1eXl5qeOmtP744w+dz3Xr1iUmJobKlSvr/RVkJkQzMzMqV66Mm5ubQdvVrVuX69evY2Jiordf7dNALy8vDh8+nG/8D/Pw8MDS0pKIiIg8YwTIysrKM4+8yjUyMjLf7pK5qVKlCm+//Ta7du2ie/fuOuMCRQ55QvaysHQA/zlQpz9sGw+XDhm2XfyBnG6MDYdBy4lgYQdA66plea9DVWZvP5PnpnvP3iJ0xxkmdfQqjCMQQgghxLNiwFTzLyJjY2O1i5yxsbHeemtra4YPH8748eMpUaIE5cuXZ9asWdy7d4/BgwcDMGzYMObOncv48eMZMmQIR48e1Xvf1nvvvUejRo0YNWoUQ4YMwdrampiYGMLDw3W6HBa2tm3b4uvrS9euXQkNDaVKlSpcvXqVrVu30q1bN3x8fHjrrbcICgrCx8eHJk2asGrVKv766y8qVaqUa54WFha89957TJgwATMzM5o0acKtW7f466+/GDx4MGXKlMHS0pIdO3ZQvnx5LCws9Ka8f7RcK1SoQGhoqE65Ps79+/cZP348PXv2pGLFivzzzz9ERkbSo0ePpy63l408IXvZONWA4O3Q7RuwLmPYNkoW/PEVLPCBE2vU2RjfbF6J7nVeyXfTb369yPqj/zxt1EIIIYQQubKzs8POzi7P9bNnz6ZHjx4MGDAAHx8fLl68yPbt23F0dARyuhyuX7+eTZs2UatWLb7++mtmzZqlk0fNmjXZv38/f//9N82aNaNOnTpMmTIFZ2fnZ3psGo2Gbdu20bx5c4KDg6lSpQp9+vQhPj5enRWxd+/eTJ48mQkTJlCvXj3i4+MZPnx4vvlOnjyZd955hylTpuDl5UXv3r3VcV0mJiZ88cUXfPPNNzg7O9OlS5dc83i4XOvWrcv58+fZuXOnWq6PY2xszO3btxk4cCBVqlShV69e+Pv7M23atAKUUPGgUeTFUoUiKSkJe3t7EhMT871pPFcPEmHfbDj8TU6jy1AVGue8VNqpOg8ysuiz+A+iLyfkmdzM2IgfhjainqthF+izlpGRwbZt2+jYsaNMrVpMyTlQvEn9F29S/zkePHhAbGyszvuuioPs7GySkpKws7PDyEieOxQ3z7v+87vOCtI2kDP1ZWZhDx0+hmG/5TSyDHXpd/imOWyfiEVWCosH1MPJLu+beXpWNm+uOMrVhPuFELQQQgghhBDFhzTIioOy1SB4G3T/FmzKGraNkgWHF8GXPpS5uJHFA+pibpL36fJvShpDV0RxP70AT+KEEEIIIYQo5qRBVlxoNFCzV85sjL6jQKM/MDZXqTdh0zBq7urD4vbm+Sb980oS7647gfSCFUIIIYQQwjDSICtuLOzAbyYMPwhuzQzf7vIftNjbgw1uG7Ej75dCbz15jS/3nC+EQIUQQgghhHj5SYOsuCrjBYG/QI+lYFvOsG2UbOpeX8sBq/G8ZrwPDdm5Jvss/G92/HmtEIMVQgghhBDi5SQNsuJMo4EaPWFUJDQeDUaGvZbOLjuBT0wXs85sGtU0sbmmeXvNCWKuJhVmtEIIIYQQQrx0pEEmwNwW2s+AYQehYnODN6tndI7NZh8y3WQZdqTorLufkcUb30fxb0paYUcrhBBCCCHES0MaZOJ/ylSFgZuh5zKwNexFiMYahYEm4ew1f4dexnt1ujFeSbjP8JVHSc/MvWujEEIIIYQQxZ00yIQujQaqd8/pxthkLBgZ9lLNkppkQk2/ZYNZCNU1F9XlkXF3mbzpT5l5UQghhBBCiFxIg0zkztwG2k2D4b9DpZYGb1bH6DybzSYz02QpDiQDsCbqMssOxj2TMIUQQgghhK6wsDAcHBye+X6CgoLo2rXrM8m7efPmrF69Wv2s0WjYtGlTnunj4uIwNjbm1KlTeabZt28fGo2GhISEx+4/JiaG8uXLk5qa9+zihUUaZCJ/pavAgE3w2nKwK2/QJkYahX4mEewxf4c+xnvQkM2MrTH8+vetZxysEEIIIV4G169fZ/To0VSqVAlzc3NcXFwICAggIiKiqEN7YmFhYWg0GjQaDUZGRpQvX57g4GBu3rxZ1KE9VlxcHBqNhujoaJ3l8+fPJywsrND3t3nzZm7cuEGfPn0KPW9DeXt706hRIz777LNnvi/DptUTxZtGA9W6gkc7+PVT+P1LyM547GYlNCnMNl1CH+M9TMkIZtRqEzaNbEKl0jbPIWghhBBCaDVc1ZAMA/7tftZMjUw53O9wvmni4uJo0qQJDg4OfPLJJ9SoUYOMjAx27tzJyJEjOXPmzHOKtvDZ2dlx9uxZsrOzOXHiBMHBwVy9epWdO3fqpc3KylIbby8qe3v7Z5LvF198QXBwcJEfe3BwMG+88QaTJk3CxOTZNZte3BoWLx4za2g7FUb8Ae5tDN6sttFFNplNYWLmIsaF7SHxftH/gyCEEEIUJxnZGS/M3+OMGDECjUbDkSNH6NGjB1WqVKFatWqMGzeOP/74A8j9iU1CQgLGxsYcOHAA+F/3tJ07d1KnTh0sLS1p3bo1N2/eZPv27Xh5eWFnZ0ffvn25d++emk/Lli0ZPXo0Y8eOxdHRkbJly/Ltt9+SmppKcHAwtra2VK5cme3btxe4HjQaDU5OTjg7O+Pv78+YMWPYvXs39+/fV7sZbt68GW9vb8zNzbl0oz+DRAAAIABJREFU6RJ3795l4MCBODo6YmVlhb+/P+fOndPJNywsjAoVKmBlZUW3bt24ffu2zvrcuhaOHTuWli1bqp+zs7MJDQ2lcuXKmJubU6FCBWbOnAlAxYoVAahTpw4ajUbd7tF809LSGDNmDGXKlMHCwoKmTZsSGRmprtfWSUREBD4+PlhZWdG4cWPOnj2rprl16xZ79uwhICBAr/yuXbuGv78/lpaWVKpUiXXr1uml+fvvv2natCkWFhZUr16d/fv351kf8fHxBAQE4OjoiLW1NdWqVWPbtm3q+nbt2nHnzp188ygM0iATBVeqMvRfD71Xgr2LQZsYaRT6muxlWcpwNiyeTmaGNMqEEEIIoevOnTvs2LGDkSNHYm1trbf+ScZFhYSEsGDBAn7//XcuX75Mr169mDdvHqtXr2br1q3s2rWLL7/8Umeb5cuXU6pUKY4cOcLo0aMZPnw4r732Go0bN+bYsWO0b9+eAQMG6DTknoSlpSXZ2dlkZmYCcO/ePebMmcOSJUv466+/KFOmDEFBQURFRbF582YOHTqEoih07NiRjP//LnX48GEGDx7MqFGjiI6OplWrVsyYMaPAsUyaNInZs2czefJkYmJiWL16NWXLlgXgyJEjAOzevZtr166xYcOGXPOYMGEC69evZ/ny5Rw7dozKlSvj5+fHnTt3dNJ98MEHzJ07l6ioKExMTBg0aJC67sCBA1hZWeHl5aWX/+TJk+nRowcnTpygX79+9OnTh9OnT+ukmTJlCm+//TbHjx/H19eXgIAAvQaq1siRI0lLS+PXX3/l1KlTzJkzBxub//XkMjMzo3bt2vz2228GlOCTkwaZeDIaDXgFwMgj0OxdMDYzaDNHTQrBd7/g5udN4Z+oZxykEEIIIf5Lzp8/j6IoVK1atdDynDFjBk2aNKFOnToMHjyY/fv3s2jRIurUqUOzZs3o2bMne/fu1dmmVq1afPjhh3h4eDBp0iQsLCwoVaoUb7zxBh4eHkyZMoXbt29z8uTJJ47r3LlzfP311/j4+GBrawtARkYGCxcupHHjxnh6enLlyhU2b97MkiVLaNasGbVq1WLVqlVcuXJFneBi/vz5dOjQgQkTJlClShXGjBmDn59fgWJJTk5m/vz5hIaGEhgYiLu7O02bNmXIkCEAlC5dGoCSJUvi5OREiRIl9PJITU1l0aJFfPLJJ/j7++Pt7c23336LpaUlS5cu1Uk7c+ZMWrRogbe3NxP/j707D6uqWh84/j3nMMskswqOODIqoCLOE5qWpmk2OKWWppZpafVrvN2ybDLTtGs3u+VUVmqpaTgP4YSCoIKzKMqkDMp4pt8fJ0+R09kIiPJ+nmc/j5yz91rvZgPystZ618sv88cff1BcXAyYRq28vb1vOF1xyJAhjB07lmbNmvHOO+8QHh5+XTI9btw4Bg8eTMuWLZk/fz4uLi7X9X9NamoqUVFRBAUF0bhxY/r370/nzmX35K1bty5nz5618DNZPpKQiTtj4wA9XjdNY/TvZfFldQuT4asesHoSFGRXYoBCCCGEuFdUxjY5wcHB5n97e3vj4OBA48aNy7z2z8Iaf79Go9Hg7u5OUFBQmWsAxQU58vLycHR0xMHBgebNm+Pt7c2SJUvM79vY2JTp++jRo1hZWdGuXTvza+7u7jRv3tw8MnT06NEy7wNERkYqiuvo0aOUlJTQo4flS1L+6eTJk2i1WqKiosyvWVtb07Zt2+tGsf5+j3Xq1AH++lwWFRVhZ2d3wz7+eV+RkZHXtR0REWH+t5WVFeHh4dedc81zzz1nTtjffPPNGybY9vb2dzwSejuSkImK4d4EnlgBw5aCa33Lrzv4HXweBnsXgkFfefEJIYQQotpr2rQpKpXqtoU7ro2e/D2B095kOYS19V97qqpUqjIfX3vNYDDc9JobXadSqQCuu+52nJyciI+PJykpiYKCArZv306zZs3M79vb25vbrkhqtfq6ZPfvny97e/sK7/NWbvW59PDwICcnp0riGDt2LKdOnWL48OEkJibecMTt8uXL5hHCyiIJmag4KhW06GeaxthlBkaNrWXXFefCuhdhYTc4t7dyYxRCCCFqIGu1dbU5bsXNzY3o6GjmzZt3w/2fru0fde0X5IsXL5rf+2dJ9upIrVbj7+9P48aNLUqCWrZsiU6nY8+evypTXrp0iZSUFFq1amU+5+/vA+biJ9d4enqW+VxB2c9X06ZNsbe3v+m2AjY2pqUpev3N/3jepEkTbGxs2LVrl/k1rVbLvn37zLFaonXr1qSnp98wKfvnfe3evfu6tWb79/+1JEan0xEXF3fD9WjX+Pn5MX78eH7++WemTZvGwoULy7yflJRE69atLY6/PKTsvah41vbQ7VVUIcPIX/UizqkW7hlyMQH+2wtCn4Seb4Fj5f41QgghhKgpbldqvjqZN28eUVFRtG3bln/9618EBwej0+mIiYlh/vz5HD16FHt7e9q3b8/7779Po0aNyMzM5LXXXrurcbdo0YKZM2fy8MMPV1ibTZs2ZcCAAYwbN44vv/wSJycnXn75ZerVq8eAAQMA07S7qKgoPvroIwYMGMCGDRtYv359mXa6d+/Ohx9+yLfffktkZCSLFy8uk2jY2dkxY8YMpk+fjo2NDVFRUWRlZXH48GHGjBmDl5cX9vb2rF+/Hl9fX+zs7K4reV+rVi0mTJjASy+9hJubG/Xr12fWrFkUFhYyZswYi++5devWeHh4sGvXLvr371/mvRUrVhAeHk7Hjh1ZsmQJe/fuvW592FdffUVgYCABAQF8+umn5OTklCka8ndTpkyhb9++NGvWjJycHLZs2VImeTtz5gxpaWn07NnT4vjLQ0bIROVxa4zzUz+zL/ILUg0Kkqv4xTA3DPb8B/S6yotPCCGEENVO48aNOXDgAN26dWPatGkEBgbSq1cvNm3axPz5883nff311+h0OsLCwpgyZUq5KgtWpJSUFPLy8iq83UWLFhEWFkb//v2JjIzEaDSybt0687S/9u3bs3DhQj777DNCQkL4/fffr0tOo6Ojef3115k+fToRERFcuXKFESNGlDnn9ddfZ9q0abzxxhu0bNmSRx991Lyuy8rKijlz5vDll19St25dczL4T++//z6DBw9m+PDhtGnThhMnTrBhwwZq165t8f1qNBpGjx5dZm3dNW+//TbLly8nODiYb7/9lmXLll03+vbmm28ya9YsQkJC2LlzJ7/88gseHh437Euv1zNx4kRatmxJnz59aNasGV988YX5/WXLltG7d28aNGhgcfzloTJWxurJGig/Px8XFxfy8vJwdna+2+FUO3PWH0K/YzYTrH7BTqWg5L13EPT7COq3t/gSrVbLunXreOCBB66bAy5qBvkaqNnk+dds8vxNiouLOX36NI0aNbppgYT7kcFgID8/H2dn57u+qbAov/T0dAICAjhw4ICiZKgin39paSlNmzZl6dKlZQqV/N2tvs+U5AbylSqqxKTeQRxrOZGepR8Sow+z/MKMRPg6GlaOh6vKKhkJIYQQQoh7j4+PD//9739JTU29azGkpqby6quv3jQZq0iSkIkqoVar+HhoCM4+/ozTTmNU6UucMXhb3kDCMlM1xt0LZBqjEEIIIcR9buDAgXTq1Omu9e/v788zzzxTJX1JQiaqjIONFQtHhuPhaMNWQ2uiSz/gI+0QioyWbSpNST6snwH/6QJn/6jcYIUQQgghhKgCkpCJKlXP1Z4FT4ZhrVFRgg1z9Q/Tq/RDNujDLW8kIwkW9YWfn4Yr6ZUXrBBCCCGEEJVMEjJR5cIbuvHuw3/tdn/e6Mkz2qmMLJ3BaSXTGA99D5+HQ+w80CsoFCKEEELUEFK7TYjKU1HfX5KQibtiaLgfYzo2KvPaNkMI0aWzmKUdavk0xtIrsOFV+LIznNlZCZEKIYQQ955rFSYLCwvvciRC3L+ufX/daUXXu7ox9MyZM/n5559JTk7G3t6eDh068MEHH9C8eXPzOV27dmXbtm1lrnvmmWdYsGCB+ePU1FQmTJjAli1bcHR0ZOTIkcycORMrq79ub+vWrUydOpXDhw/j5+fHa6+9xqhRo8q0O2/ePD788EPS09MJCQnh888/p23btpVz84JX+rbgeOZVth/LMr9WijVf6AeySt+R16wX84Bmr2WNZR6Bb/pB0BDo9kYlRSyEEELcGzQaDa6uruZ9pBwcHFCpVHc5qspnMBgoLS2luLhYyt7XQFX1/I1GI4WFhWRmZuLq6opGo7mj9u5qQrZt2zYmTpxIREQEOp2OV199ld69e3PkyBFq1aplPm/cuHH861//Mn/s4OBg/rder6dfv374+Pjwxx9/cPHiRUaMGIG1tTXvvfceAKdPn6Zfv36MHz+eJUuWsGnTJsaOHUudOnWIjo4G4Pvvv2fq1KksWLCAdu3aMXv2bKKjo0lJScHLy6uKPiM1i5VGzeePtebhL3ZxKqugzHsX8OBZ7RQ66Q/xL+v/0Uh10bJGE1dglbKOJh4Pgb4X1OA9aIQQQtRsPj4+AOakrCYwGo0UFRVhb29fIxJQUVZVP39XV1fz99mdqFYbQ2dlZeHl5cW2bdvo3LkzYBohCw0NZfbs2Te85rfffqN///5cuHABb2/T+qMFCxYwY8YMsrKysLGxYcaMGaxdu5akpCTzdcOGDSM3N5f169cD0K5dOyIiIpg7dy5gyrD9/PyYPHkyL7/88m1jl42hy+9U1lUGzttFfvGNy9nboOVF542MM/6ESmv51AujR3NUfWaCf4+KClXcI2Rj2JpNnn/NJs//enq9Hq22Zqy11mq1bN++nc6dO8vzr4Gq8vlbW1vfcmRMSW5wV0fI/ikvLw8ANze3Mq8vWbKExYsX4+Pjw4MPPsjrr79uHiWLjY0lKCjInIwBREdHM2HCBA4fPkzr1q2JjY2lZ8+eZdqMjo5mypQpgGkn7ri4OF555RXz+2q1mp49exIbG3vDWEtKSigpKTF/nJ+fD5i+EGrKD72K4udqy+xHgxn77QEMN/jzQCnWvJffl0MNejO79gqskn+xqF1VdgosHoShURf03d6AOiEVHLmorq59D8r3Ys0kz79mk+d/Y3c6pepeYTAY0Ol0aDSaGnPP4i9V+fwNBgMGg+Gm7yv5GVRtEjKDwcCUKVOIiooiMDDQ/Prjjz9OgwYNqFu3LocOHWLGjBmkpKTw888/A5Cenl4mGQPMH6enp9/ynPz8fIqKisjJyUGv19/wnOTk5BvGO3PmTN5+++3rXv/999/LTKkUlhvQQMXKMzf/5llzVkN+yVDGN2lB0PnvcCqxbBqj+vQ21Kd7cK52JMl1HqHQ1rOiQhbVXExMzN0OQdxF8vxrNnn+NZs8/5qtOjx/JQV1qk1CNnHiRJKSkti5s2ylvKefftr876CgIOrUqUOPHj04efIkTZo0qeowzV555RWmTp1q/jg/Px8/Pz969+4tUxbLqa/RiNXqI6yIS7vpOdvT1US3e5yIIVPQ7/0S9Y6PUGkLbnr+3/nlxOKbtx9D2FMYOk4FB/eKCl1UM1qtlpiYGHr16iVTVmogef41mzz/mk2ef81WnZ7/tdlzlqgWCdmkSZNYs2YN27dvx9fX95bntmvXDoATJ07QpEkTfHx82Lu3bCW+jIwM4K/FrD4+PubX/n6Os7Mz9vb25mHNG51zs4V6tra22NraXve6tbX1Xf8CuJe9+3AwZy4Vsu9Mzk3PeWvNUZr6uNC281QIHQa/vwZJP1nUvsqgRbPvSzSHlkHU89D+WbCREc37lXw/1mzy/Gs2ef41mzz/mq06PH8l/d/VeqBGo5FJkyaxcuVKNm/eTKNGjW57TXx8PAB16tQBIDIyksTExDIVhGJiYnB2dqZVq1bmczZt2lSmnZiYGCIjIwGwsbEhLCyszDkGg4FNmzaZzxFVw8ZKzfwnw6jnan/Tc7R6I+MXx3HuciE414VHvoaRv4JnC8s7KsmHze/AnNYQ9w3ob1xQRAghhBBCiMp0VxOyiRMnsnjxYpYuXYqTkxPp6emkp6dTVFQEwMmTJ3nnnXeIi4vjzJkz/PLLL4wYMYLOnTsTHBwMQO/evWnVqhXDhw8nISGBDRs28NprrzFx4kTzCNb48eM5deoU06dPJzk5mS+++IIffviBF154wRzL1KlTWbhwIf/73/84evQoEyZMoKCggNGjR1f9J6aG83C0ZeGIcBxsbr6e7HJBKeO+3U9ByZ+JVKPOMH4n9P43RjsXyzu7mg6/Pg/zO0DyWqg+RUeFEEIIIUQNcFcTsvnz55OXl0fXrl2pU6eO+fj+++8B08jVxo0b6d27Ny1atGDatGkMHjyYX3/91dyGRqNhzZo1aDQaIiMjefLJJxkxYkSZfcsaNWrE2rVriYmJISQkhI8//pivvvrKvAcZwKOPPspHH33EG2+8QWhoKPHx8axfv/66Qh+iarSq68wnQ0NveU5y+hWmfB+P4VppRo01dJiM7tn9HPfqi1Fz/ZTSm8pOgeWPw9d9IHXPHUQuhBBCCCGE5e7qGrLbbYHm5+fHtm3bbttOgwYNWLdu3S3P6dq1KwcPHrzlOZMmTWLSpEm37U9UjT6BPkzt1YxPYo7d9JyYIxl8EnOMF6Ob//WifW2O1HuMhkPfw3rHLEhYDlg48nVuN3zdG1r0hx5vgmezO7sJIYQQQgghbuGujpAJcTuTu/vTL7jOLc+Zu+UEvyRcuP4NFz94eAGM3wH+Pa9//1aS18AX7U3TGa+kK7tWCCGEEEIIC0lCJqo1lUrFR4+EEFjv1lsJvLQigUPnc2/8pk8QPPkTjPgF6tx6GmQZRr2p4Mec1rD531BseflSIYQQQgghLCEJmaj27G00/Gd4OB6ON18TVqIz8PS3cWTmF9+8ocZdYNwWU1XG2g0tD0BbCNs/hDmhsHsB6Eotv1YIIYQQQohbkIRM3BPqutrznxFh2Ghu/iWbnl/MuO/iKNHqb96QWg2Bg2HiPug7S9nm0IWXYP0MmBcBiT+CwaDgDoQQQgghhLieJGTintGmfm1mDgq65TkJ53L5v9VHbl+93soG2j0Dz8VD55fAWsHm0Dln4KcxsLAbnNpq+XVCCCGEEEL8gyRk4p4yOMyXpzs3vuU5qxMusvmCyrIG7Zyh+2vw3EEIGw2qm+99dp2L8fDtAPhuEKQnWn6dEEIIIYQQf5KETNxzZvRpQbfmnrc859dUNZtTsixv1MkHHpwNz+42lbxX4uQmWNAJfn4GclOVXSuEEEIIIWo0ScjEPUejVvHZY63x93K86TlGVExdcYhjGVeUNe7ZDIYtgTExUD9SwYVGOLQcPg+DDf8HhZeV9SuEEEIIIWokScjEPcnZzpqvRoTjYm9903MKSvQM+89u5m89yZVirbIO/NrC6N9g2DLwaH7786/Rl0LsXPgsFHZ+CtoiZf0KIYQQQogaRRIycc9q6FGLL55og0Z98/VilwtK+WB9Mh3e38yHG5LJvlpieQcqFbR4ACb8AQ/OAadbb1BdRkkebHzLNGJ2cDEYblH5UQghhBBC1FiSkIl7WpS/B2/0b3Xb864U65i35SQdP9jMW78cJi1XwciVxgrCRsLkA9DjDbC99SbVZeSnweqJMD8KUtZz+/KPQgghhBCiJpGETNzzRkQ24LG29S06t1hr4Js/ztBl1hZeXJHAicyrlndk4wCdpplK5bd/FtQ3ny55nayjsOxR+KYfnN9v+XVCCCGEEOK+JgmZuOepVCrefiiAto3cLL5GZzDyY9x5en26jQmL40g8n2d5h7Xcoc9MmLwfgoYoC/bsLviqB/wwAi6dVHatEEIIIYS470hCJu4LNlZqFg4Pp+ttyuH/k9EIvyWl8+DcnQz/7x52n7qE0dJphbUbwuCv4Olt0LirsoCPrIZ5bWHNVLiaqexaIYQQQghx35CETNw3XBys+XpkBJ8PC8GvlvK1WjuOZzPsP7sZPP8PNh7JsDwxqxsKI1bD8JXgE2R5hwYd7P+vqSLjlplQorBEvxBCCCGEuOdJQibuK2q1ij4B3kwL0vP1yDa0b2z5NMZrDqTmMvbb/fT9bAer49PQ6Q2WXdikOzy9HQYtBFfL1rQBoC2Abe/DnNawdyHoFZboF0IIIYQQ9yxJyMR9SaWCTv4eLH86kp8mdKBnSy/FbSSnX+H55fF0/3gbS/acpVhrQel6tRqCh8Kk/RA9E+wVJIQFWbDuRdNUxsMrpSKjEEIIIUQNIAmZuO+FNajNVyMjWD+lEwNC63KLbctuKPVyIf+3MonOs7bwn+0nuVqiu/1FVrYQ+Sw8Hw8dp4KVveUdXj4FK0aZin+c2aksWCGEEEIIcU+RhEzUGC18nPlsWGu2vNiVx9vVx0aj7Ms/80oJ761LJur9zXwSc4ycgtLbX2TnAj3fhOcOQOvhoFLQZ1qcqUz+kqGQcVhRrEIIIYQQ4t4gCZmocRq41+K9h4PYMaMbT3duTC0bjaLr84q0zNl0nKgPNvPOmiOk5xXf/iLnujBgLkyIheYPKAv4+AbTxtKrnoW888quFUIIIYQQ1ZokZKLG8na249UHWrLr5e680LMZrg4KNnoGCkv1/HfnaTrN2szLPx3idHbB7S/yagGPLYPRv4FvhILejBC/BD4Pg5g3oChHUaxCCCGEEKJ6koRM1HiuDjY837Mpu2Z057V+LfFxtlN0vVZvZPm+c/T4eCuTlh7g8AULNplu0AHGxMDQ78Dd3/LOdMWw6zNTqfxdc0BrweicEEIIIYSotiQhE+JPtWytGNupMdumd+WDwUE0dHdQdL3BCGsOXaTfnJ2MXrSXfWcu3/oClQpaPQTP7oZ+n0AtBZUgi3Mh5nWYGw7xy8BgQQVIIYQQQghR7UhCJsQ/2FppeDSiPpumdWXu461pWcdZcRtbUrIYsiCWoQti2ZKSeetNpjXWEDEGnjsI3f4PbBwt7yjvHKwaD192huMxUipfCCGEEOIeIwmZEDehUavoH1yXdc91ZNHoCCIa1lbcxt4zlxm9aB/95uxkzaEL6A23SJhsHaHLdHguHto+A2oryzvKSIIlj8D/HoS0A4rjFEIIIYQQd4ckZELchkqloltzL1aM78CK8ZF0a+6puI0jF/OZtPQgPT/Zxvf7UinVGW5+sqMnPDALJu2DgEHKOjqzAxZ2gxWjTfuZCSGEEEKIak0SMiEUiGjoxqLRbVn7XEf6B9dBpXCT6dPZBcz4KZHOs7bw352nKSy9xSbTbo1hyCIYtxkadlLW0eGfYW4E/Po85JxRdq0QQgghhKgykpAJUQ4BdV2Y+3gbNk/ryrAIP6w1yjKz9Pxi3llzhKj3NzNn03HyCrU3P7leGIz8FZ74EbwCLO/EoIO4b2BOG1g5AbJPKIpRCCGEEEJUPknIhLgDjTxq8f7gYLZP78aYjo2wt1a2yXROoZZPYo7R4f1NzFx3lMz8m5SxV6mgaS8YvwMGLgBnX8s7MeohYSnMi4Afn4KMI4piFEIIIYQQlUcSMiEqQB0Xe17v34pdL3fnuR5NcbZTUJADKCjV8+X2U3SctYVXVyaSeqnwxieqNRD6GEyOg17vgJ2r5Z0YDZD0E8yPhOVPwIV4RTEKIYQQQoiKJwmZEBXIrZYNU3s1449XevDqAy3wcrJVdH2pzsDSPal0/WgLzy8/SHJ6/o1PtLaDqOfg+Xjo8BxolPVD8hr4TxdYMgTO7VV2rRBCCCGEqDCSkAlRCRxtrXi6cxO2T+/Guw8HUt9N+SbTq+Mv0Gf2Dsb+bx8HUnNufKJ9bej9jmnELPQJQGGVkeO/w397wf8egtM7ZB8zIYQQQogqJgmZEJXIzlrDE+0asHlaFz4bFkpzbyfFbWw8msmgL/5g2H9i2X4s68abTLv6wcAvYMIuaPmQ8kBPb4P/9YdFfeHERknMhBBCCCGqiCRkQlQBK42aAaH1+O35Tnw1Ipw29RWs/frT7lOXGfH1Xh6au4v1SRcx3GiTae8AePQ7eHY3BA0BlcJv8dRYWDwYFnaH5HWSmAkhhBBCVDJJyISoQmq1ip6tvPlpQgeWP92eTk09FLeRmJbH+MUH6PXpNlbsP4dWf4NNpr1awuCvYNJ+CH0S1MqKjHDhACx/DBZ0gsMrwXCLjayFEEIIIUS5SUImxF2gUqlo39id78a049dJHekb6KN4k+mTWQW89OMhun64lW92naaoVH/9Se5NYOA8mHwAwp8CjY2yTjISYcUo+KI9JHwP+ltsZC2EEEIIIRSThEyIuyzI14X5T4YR80IXHgnzxUqtLDNLyy3irV+P0PGDzczbcoK8ohtsMl27AfT/FJ5PgPbPgpW9siCzU2Dl0zA3HA58C7pSZdcLIYQQQogbkoRMiGrC38uRj4aEsG16N0Z1aIidtbJvz0sFpXy4IYWO72/mg/XJZF0puf4k57rQZyZMOQRRU8DGUVmQOafhl8nweRvYuxC0N9nIWgghhBBCWEQSMiGqmXqu9rz1UAA7Z3RnYrcmOCncZPpKiY75W0/S8YPNvLgigfhzuddXZnT0gl5vw5RE6DIDbF2UBZl3Dta9CJ+FQOw8KC1Qdr0QQgghhAAkIROi2vJwtOWl6Bbserk7M/q0wMNR2fqvEp2BH+POM3DeLvp/vpNle1MpKPnHGjAHN+j2KryQCN1fB3s3ZUFeTYcNr8LsYNjxCRTfZCNrIYQQQghxQ5KQCVHNOdtZM6FrE3bO6M47AwKo56pw/Rdw+EI+r/ycSPv3NvHG6iRS0q+UPcHOBTq/aBox6/1vqOWlrIPCbNj0NswOgq3vQ9FNNrIWQgghhBBllCsh02q1nDt3jpSUFC5fvlzRMQkhbsDOWsPwyIZsfakrnwwNwd9L4fovTNMZv409S/Ts7QxZ8AerDqZRovtbdUZbR+gw2bTGrO+H4FxPWQfFubB1JnwaBBvfhoJsxTEKIYQQQtQkFidkV65cYf78+XTp0gVnZ2caNmxIy5Yt8fT0pEFuF6MVAAAgAElEQVSDBowbN459+/ZVZqxCCMBao2ZQG19+n9KZL4eHEeKrcP3Xn/adyWHK9/FEztzMzHVHOXvpb+vArO2h3dPwXDw8+Bm4NlDWeOkV2PmJacRs/atwJb1cMQohhBBC3O8sSsg++eQTGjZsyKJFi+jZsyerVq0iPj6eY8eOERsby5tvvolOp6N379706dOH48ePV3bcQtR4arWK6AAfVk2MYsnYdnRo4l6udi4XlPLl9lN0+XArw/+7hw2H09Fd22zaygbCRsHkOBi4ANybKmtcWwi755nWmK2dBrnnyhWjEEIIIcT9yqLybfv27WP79u0EBATc8P22bdvy1FNPsWDBAhYtWsSOHTto2lThL25CiHJRqVRE+XsQ5e9B/Llcvtpxig2H09Hqjbe/+B92HM9mx/FsfJztGNbWj2ER9fFxsQONNYQ+BsFD4cgq2P4RZB6xvGF9Cez7CuK+gZDHoNNUcGusOD4hhBBCiPuNRQnZsmXLLGrM1taW8ePH31FAQojyC/VzZe7jbci6UsIP+8+xdE8qablFittJzy9m9sbjfL75BD1bevFEuwZ09PdArdZA4GBo9TCkrIPtH8LFeMsbNujg4HcQvwSChkCnaeDZXHF8QgghhBD3izuuspifn8+qVas4evRoRcQjhKgAnk62TOzmz/bp3Vg0KoIeLbxQqZS3ozcY2XA4gxFf76Xbx1v5cttJLheUgloNLfvD01vhiR/Bt62yho0GOPQ9zGsHP4yE9CTlwQkhhBBC3AcUJ2RDhw5l7ty5ABQVFREeHs7QoUMJDg7mp59+qvAAhRDlp1Gr6NbCi/+OimDH9G5M6uaPh6Ntudo6e6mQmb8l0/69TUxZfpB9Zy5jBGjaC8b8DiN+gYadFLZqNE2BXBAFyx6DtLhyxSaEEEIIca9SnJBt376dTp1Mv3StXLkSo9FIbm4uc+bM4d///neFByiEqBi+tR14Mbo5sa9054sn2pS7CEip3sCq+AsMWRBLn9k7+Db2DFdKdNC4C4xaA09tAP+eyhtOWQcLu8N3gyB1d7liE0IIIYS41yhOyPLy8nBzcwNg/fr1DB48GAcHB/r16yfVFYW4B1hr1DwQVIel49qzaVoXxnRshIu9dbnaSsm4whurD9PuvU288vMhktLyoH57ePInGLcFmvdT3ujJTfB1NHzTH05tBaPy4iRCCCGEEPcKxQmZn58fsbGxFBQUsH79enr37g1ATk4OdnZ2FR6gEKLyNPF05PX+rdjzag8+GhJC6/qu5WqnsFTPsr3n6P/5TgbM28WK/eco8gyBx5bC+F0Q8DCgcBHbmR3w7QD4b2849rskZkIIIYS4L1lUZfHvpkyZwhNPPIGjoyMNGjSga9eugGkqY1BQUEXHJ4SoAnbWGh4J8+WRMF+S0vJYujeVVQfTKCzVK24r4VwuCedyeWfNER4J8+Pxdg3xH/INdD1m2iz60A9gVNDu+b2wdAjUCYHOL5lG3dR3XI9ICCGEEKJaUPxbzbPPPktsbCxff/01O3fuRP3nL0aNGzeWNWRC3AcC67nw3sNB7Hm1B+8MCKC5t1O52skv1vH1rtP0/GQbj/1nN2suOlL64BcweT+0GQFqhdMkLybA90+aCoAk/ggG5cmiEEIIIWoGnUF3t0OwmOIRMoDw8HDCw8PLvNavXznWigghqi0nO2uGRzbkyfYNOJCaw+LdqaxNvEipzqC4rdhTl4g9dQkPR1sejfDlsU4f4Nt5OvwxB+L+Z9o42lKZR+CnMbB1pmkfs6Ahpo2rhRBCCFEjFeuKSb6cTEJGAjEFMSz8dSE+jj581furux2aRSxKyKZOnWpxg5988km5gxFCVD8qlYqwBm6ENXDj9f6t+DHuHEv2pHL2UqHitrKvljBvy0m+2HqSbs29eLL9i3SJmopm91zY/zVoFbR56QSsmgBb34eOL0Do42BVvpL+QgghhLg36Aw6TuaeJCk7iaRLSSRlJ3Ei5wQ6499GxLRwqfgSBqMBtar6L3OwKMKDBw9adMTHxyvqfObMmURERODk5ISXlxcDBw4kJSWlzDnFxcVMnDgRd3d3HB0dGTx4MBkZGWXOSU1NpV+/fjg4OODl5cVLL72ETld2mHLr1q20adMGW1tb/P39+eabb66LZ968eTRs2BA7OzvatWvH3r17Fd2PEPc7t1o2PN25CVumdeW7MW3pE+CDRq18x2mjETYnZ/LUN/vpPP8o82xGkzV2n2nEy0bhFMncs7BmCsxpDXu+BG2R4niEEEIIUf0YjUZS81NZd2odH+z9gBG/jSByaSSP/PoIb8W+xY/HfiT5cnLZZOxPV7VXOZt/9i5ErZxFI2RbtmyplM63bdvGxIkTiYiIQKfT8eqrr9K7d2+OHDlCrVq1AHjhhRdYu3YtK1aswMXFhUmTJjFo0CB27doFgF6vp1+/fvj4+PDHH39w8eJFRowYgbW1Ne+99x4Ap0+fpl+/fowfP54lS5awadMmxo4dS506dYiOjgbg+++/Z+rUqSxYsIB27doxe/ZsoqOjSUlJwcvLq1LuX4h7lVqtolNTTzo19SQ9r5jv951j2d5U0vOLFbeVllvEhxtS+DRGRXRgf0YMfIK2mStQ7Z4PxbmWN5SfBr9Nh+0foW7/LBp9XcWxCCGEEOLuySrMIjE7kaTsJA5fOkxSdhL5pfnlbi8pO4lGLo0qMMLKoTIay19L+ty5c4CpFH5FyMrKwsvLi23bttG5c2fy8vLw9PRk6dKlPPLIIwAkJyfTsmVLYmNjad++Pb/99hv9+/fnwoULeHt7A7BgwQJmzJhBVlYWNjY2zJgxg7Vr15KUlGTua9iwYeTm5rJ+/XoA2rVrR0REBHPnzgXAYDDg5+fH5MmTefnll6+LtaSkhJKSv9a95Ofn4+fnR3Z2Ns7OzhXy+RDlo9VqiYmJoVevXlhby9qiqqLTG9h6LJule8+x48SlO2qrsYcDw1u7MdS4AYcDX6IqzFbcRonGEXXbsRAxBpzq3FE84t4iPwNqNnn+NZs8/3vHldIrHLl8hMOXDpuPzKLMCu1jWLNhTA+fXqFtWio/Px8PDw/y8vJumxsoTsh0Oh1vv/02c+bM4erVqwA4OjoyefJk3nzzzTv64j9x4gRNmzYlMTGRwMBANm/eTI8ePcjJycHV9a/9kRo0aMCUKVN44YUXeOONN/jll1/KTJc8ffo0jRs35sCBA7Ru3ZrOnTvTpk0bZs+ebT5n0aJFTJkyhby8PEpLS3FwcODHH39k4MCB5nNGjhxJbm4uq1evvi7Wt956i7fffvu615cuXYqDg0O5PwdC3A+yi+GPDDW7M1UU6JRPabzGWmWkvXsRY+020zZ3HfY6BSNmfzKg4YJrBKe8epHj4A+q8scjhBBCCOW0Ri0X9Rc5rz9Pmi6NNH0a2Qblf2xVyk/jxzNOz1R6PzdSWFjI448/blFCprjK4uTJk/n555+ZNWsWkZGRAMTGxvLWW29x6dIl5s+fX66gDQYDU6ZMISoqisDAQADS09OxsbEpk4wBeHt7k56ebj7n2sjY39+/9t6tzsnPz6eoqIicnBz0ev0Nz0lOTr5hvK+88kqZYifXRsh69+4tI2R3mfx1rHoYAZToDGw4nMGyfefYf1Z5MqU1qtiR7cAO+hPiM5hX68QRcf5/qK+kWdyGGj2+ubvxzd2NoU5rDBHjMLYcIAVA7mPyM6Bmk+dfs8nzv/t0Bh2n8k6ZphxeSuLI5SOczDt5w3VelUmFCicXJ/pG90V1F/4Ym59v+VRLxQnZ0qVLWb58OX379jW/FhwcjJ+fH4899li5E7KJEyeSlJTEzp07y3V9VbO1tcXW9vpf6KytreUHQDUhz+Lus7aGweH1GRxen5T0Kyzdc5afD6RxpUT5D+WE9BIeTQ/E1fZD3qyfSL+8pdgoXKyrvngQ9S/Pwqa3IPwp0+HkfdvrxL1JfgbUbPL8azZ5/lXDaDRy7sq5Muu+jl46SrFe+ZryO1XPsR6t3FqhydQwOGowQd5B1LKuVeVxXKPk609xQmZra0vDhg2ve71Ro0bY2NgobQ6ASZMmsWbNGrZv346vr6/5dR8fH0pLS8nNzS0zSpaRkYGPj4/5nH9WQ7xWhfHv5/yzMmNGRgbOzs7Y29uj0WjQaDQ3POdaG0KIO9Pcx4m3BwQyvU8Lfk24wOI9Z0lKU75QN7cEXjgexIv8m+e8DjHa8BPOV08pa6QgE7a9Dzs+hoCHof14qBemOBYhhBCiJskszDSVm//zOHzp8B0V3SgvNzs3Aj0CCXQPJNAjkACPANzs3NBqtaxbt44w77B7KiFXnJBNmjSJd955h0WLFplHiEpKSnj33XeZNGmSoraMRiOTJ09m5cqVbN26lUaNylZBCQszfTI3bdrE4MGDAUhJSSE1NdU8XTIyMpJ3332XzMxMczXEmJgYnJ2dadWqlfmcdevWlWk7JibG3IaNjQ1hYWFs2rTJvIbMYDCwadMmxfckhLi1WrZWDGtbn0cj/Dh0Po/Fu8/y66ELFGuVbTitR8Onma2ZTQiP2B/gRftf8S48riwYgxYSfzAdvhHQbjy0fAisyvfHJSGEEOJ+kV+az+Hsw38lYJeSyCys2KIblqhlXYtW7q3KJGB1atW5K9MQK4tFCdmgQYPKfLxx40Z8fX0JCQkBICEhgdLSUnr06KGo84kTJ7J06VJWr16Nk5OTec2Xi4sL9vb2uLi4MGbMGKZOnYqbmxvOzs5MnjyZyMhI2rdvD0Dv3r1p1aoVw4cPZ9asWaSnp/Paa68xceJEc8I4fvx45s6dy/Tp03nqqafYvHkzP/zwA2vXrjXHMnXqVEaOHEl4eDht27Zl9uzZFBQUMHr0aEX3JISwjEqlIsTPlRA/V17r14qfDpxnyZ6znMwqUNSOETUrisJZURRGd/VBZjiup3lp0u0v/Kfz+0yHo4+pMmPYKHCULS+EEELc/4p1xSRfTi6z2fLd2MPLWm1NC7cWBLgHEOgRSJBHEA1dGt4TmzvfCYsSMhcXlzIfXxutuqa8Ze+vrTfr2rVrmdcXLVrEqFGjAPj0009Rq9UMHjyYkpISoqOj+eKLL8znajQa1qxZw4QJE4iMjKRWrVqMHDmSf/3rX+ZzGjVqxNq1a3nhhRf47LPP8PX15auvvjLvQQbw6KOPkpWVxRtvvEF6ejqhoaGsX7/+ukIfQoiK5+JgzVMdGzE6qiG7T11myZ6zbDicjlavpAisis2GNmzOb0OA6jRPW2+gnyYWK6NWWTBX02HLu7D9QwgcDO2egbqtlbUhhBBCVFM6g46TuSdJyk4iMTuRw5cOczznOHqjvkrjUKGiiWuTMiNfzWo3w1pz70w1rCh3tA+Z+Et+fj4uLi4WlbYUleva/OEHHnjgnpo/LMrKulLCD/vPsXRPKmm5ReVqw508hmm2MNpmIx7Gy+UPxq+9KTFr+SDUwP8o7jXyM6Bmk+dfs8nzL6u6Fd34e/LVyr0VDtYVu1VUdXr+SnIDxWvIrsnMzCQlJQWA5s2bm9dvCSFERfB0smViN3/Gd2nC9mNZLN59ls0pmSj5E9IlXJinH8iXRf3pq97LKKsNhKkVrjMDOLfbdDjV/Ws6Yy0P5e0IIYQQlajaFd34MwG7VnRD3JjihCw/P5+JEyeyfPly9HrT0KZGo+HRRx9l3rx5101vFEKIO6FRq+jWwotuLbw4n1PI8r3nWL7vHNlXSyxuQ4cVvxo68GtpB4JVJxlptYEH1bHYqBROz7hyATa/A9tmQdAQ06hZnWCFdySEEELcOa1ey9HLR0nISjAf6QXpVR5HLetaBLgHEOARQKC7ad2XTy2f+6roRmVTnJCNGzeOgwcPsmbNmjIbQz///PM888wzLF++vMKDFEIIAN/aDrwY3Zznezbl98MZLNlzlj9OXlLUxiFjE6Zpn+V9HucxzWaetNqIl0rhptX6EohfbDoaRJkSs+b9QFPuSQdCCCHELWUVZpVJvo5cOkKJ3vI/TlaEvxfdCPIMItA9sEYU3ahsin97WLNmDRs2bKBjx47m16Kjo1m4cCF9+vSp0OCEEOJGrDVq+gXXoV9wHU5mXWXpnlR+jDtPXpHlBTyycGWOfhDz9Q/RV72Hp6zWE6o+qTyYs7tMh7MvtB0LbUaCg0zLEEIIUX5ag5Zjl48RnxVPQlYCh7IOkXY1rUpjuK7ohmcgzVxrZtGNyqY4IXN3d7/htEQXFxdq165dIUEJIYSlmng68nr/VrwU3Zw1hy6ybG8qcWdzLL5eixW/GKL4pTSKUNUJRlmtp596D9ZKpzPmn4eNb8HW9yF4KLR9BnwClbUhhBCiRrpUdKnM6Nfh7MNVXnjjWtGNII8gAtwDKqXohrgxxQnZa6+9xtSpU/nuu+/w8fEBID09nZdeeonXX3+9wgMUQghL2FlreCTMl0fCfDmVmcfHK7aRUuzMCQX7msUb/ZmincR7PMETVpt4XLMRT5XCxdC6Yjjwrelo2OnP6YwPgFqj8I6EEELcj3QGHcdzjpdJwM5dOVelMbjZuZkSrz/XfQV6BFLbTgZW7haLErLWrVuXWZh3/Phx6tevT/369QFITU3F1taWrKwsnnnmmcqJVAghLORX24HevkY+7duBE9nFrI5P45eEC1zMs+yvjZnU5lPdI8zTDaCfejejrdYTrD6tPJAzO0yHS31oOw7aDAd7+Q9PCCFqkpziHA5lHTInX4nZiRTpyredS3n8vehGkIdp3ZcU3aheLErIBg4cWNlxCCFEhVOpVLSq60yrus7M6NOCvWcuszr+AusSL1q03qwUa1YaOrGytCNtVMcZbbWevuq9WKkMygLJS4WY12HrTAh+1DRq5tWynHclhBCiutIb9JzIPVFm9Ots/tkq699KbUWL2i1MBTf+XPslRTeqP4sSsjfffLOy4xBCiEqlVqto39id9o3deeuhVmw/ls2q+DQ2HsmgRHe7BEvFAWMzDmib4cOlP6czbsJddUVZENpCiFtkOhp1gXbjoVm0TGcUQoh7VF5Jnnn0Kz4rnqTsJAq0lk+Vv1Me9h6EeIaYj1burbCzsquy/kXFsCghMxqNMqwphLhv2Fpp6NXKm16tvLlSrOX3wxmsik9j14lsDLfZeDoddz7WDWWubiAPamIZrVlPgLocf/08vc101G4IbZ+G0CfA3rVc9yOEEKLyGYwGTuWeMo98xWfFczqvHNPZy0mj0tDcrbk5+Qr1CqVurbryO/p9wKKELCAggDfeeINBgwZhY2Nz0/OOHz/OJ598QoMGDXj55ZcrLEghhKgsTnbWDA7zZXCYL1lXSlhz6AKr4y8Qf+7We5OVYMOP+i78qO9MhCqFkVYb6KPep3w6Y84Z2PAqbH4XQh8zVWf0bFb+GxJCCFEhrpReITEr0Zx8JWYlckWrcGbEHXCzcyPYM9iUfHmGEuARgL2VfZX1L6qORQnZ559/zowZM3j22Wfp1asX4eHh1K1bFzs7O3Jycjhy5Ag7d+7k8OHDTJo0iQkTJlR23EIIUeE8nWwZHdWI0VGNOJNdwOr4C6yOT+NU9q2mn6jYZ2zBPm0L6pLNk1YbeUyzmdqqq8o61xbAvq9MR5PupumM/r1ALfP+hRCishmMBs7knyEh86+1XydzT2LkNtMmKohapaZZ7WZ/jX55huLr5CujXzWERQlZjx492L9/Pzt37uT7779nyZIlnD17lqKiIjw8PGjdujUjRozgiSeekL3IhBD3hYYetXi+Z1Oe6+FPUlq+uVJj5pWSm15zAQ9m6YbxmW4QAzS7GK3ZQEt1qvLOT242HW6NTSNmoY+DnfMd3I0QQoi/K9AWkJidSHzmXxsv55cq3ObkDrjYupRJvgI9AmXPrxpM0T5kHTt2pGPHjpUVixBCVDsqlYogXxeCfF145YGW7Dl1iVXxafyWlM6VYt0NrynBhh/03fhB35V2qmRGWa2nt3o/GpXCv7RePgXrZ8Dmd0xrzNo+DR7+FXBXQghRcxiNRlKvpJqTr4SsBE7knsBgVDjFvJxUqPCv7V8mAWvg3EBGv4SZ4o2hhRCiptKoVXTw96CDvwf/GhDI1pRMVh28wObkTEr1N/qPXcUeY0v2aFviq8riSU0Mj2k246IqVNZx6VXY+6Xp8O9lms7YpLtMZxRCiBsoMZawL2Mfhy8fNo9+5ZTkVFn/TjZOZdZ+BXkE4WjjWGX9i3uPJGRCCFEOdtYa+gTWoU9gHfKKtGxISmd1Qhp/nLyE8QYDYeeNnryve5zPdIMYqNnFKM0GmqvPK+/4RIzpcPf/czrjY2DrdOc3JIQQ96jsomziMuI4kHGAAxkHOJZ3DMOmqhn9Amji0oQQL1PyFeIZIvt+CcUkIRNCiDvkYm/N0Ag/hkb4kZFfzK8JpkqNiWl5151bhB3L9D1Ypu9OpPoIozXr6ak+gFrpdMZLJ+C3l0zTGVs/CRFjwb1JBd2REEJUT0ajkbSraaYELPMAcRlxVbrxsqO1I0EeQeYELMgzCGcbWeMr7owkZEIIUYG8ne0Y26kxYzs15kTmVX5JMFVqPHvpn9MUVcQaAog1BOCnymCEJoZHNVtxVjqdsSQfdn8Bu+ebNplu9ww07gayNkEIcR+4tvdXXEYccZlxxGXEkVmYWWX9N3RuaN7zK8QzhMYujdGoNVXWv6gZJCETQohK4u/lyNRezXihZ1MSzuex6mAaaw5dIPtqaZnzzhm9eVf3JJ/qHmGQZgejNBvwV19Q2JsRjq03HR7Nod3TEDRUqjMKIe4pOoOO5MvJpgTsz1GwvJLrZxtUBgcrB4I8ggj2DCbUK5Rgj2Bc7VyrpG9Rs0lCJoQQlUylUhHq50qonyuv9WvJHydNlRo3JKVTUKo3n1eIHYv1vVis70lHdRKjNOvpro5XPp0xOwXWToMNr0HLB01l8xt1BvmrrhCiminWFZOYnciBDNP0w4SsBAp1CmcKlFN9p/plRr/8Xf1l9EvcFRWWkPXs2ZNTp05x6tSpimpSCCHuO1YaNZ2bedK5mSfFD+vZeDSD1fEX2JqSiVZ/LfFSsdMQxE5DEA1U6YzQxDBEsxVnVZGyznRFkPiD6XD2hZBhpuRM1poJIe6SK6VXiM+MN6//SspOQmvQVnq/9lb2BLgHmJOvYM9g3OzcKr1fISxRYQnZww8/THZ2dkU1J4QQ9z07aw39g+vSP7guuYWl/JaUzqqDaew5fdl8zlmjD+/ohvOJ7hEGa7YzUvM7TdQXlXeWfx52fGQ6/NqZErOAh8HOpQLvSAghyrpUdImDmQfNUxBTclKqZP+verXqmQpv/JmANavdDCu1TAwT1VOFfWVOnDixopoSQogax9XBhsfa1uextvW5kFvErwkXWBV/gaMX8wEowJ5v9dF8p+9FZ3UiozTr6aZJKF9n5/aYjt9m/G1KYxeZ0iiEuGMXrl4os/7rdN7pSu9ThYpmtZsR5h1GiEcIlw9d5tH+j2JtbV3pfQtREeRPBUIIUc3UdbXnmS5NeKZLE45lXGF1fBqr4y9wPqcII2q2GULYZgihke4iIzS/M0SzDUdVsfKOdMWQuMJ0ONX9a0qjR9OKvykhxH3HaDRyOu+0ufrhgYwDXCwoxwi+QlYqKwI8Amjj3YZw73BCvULNpee1Wi3rktZVegxCVCSLErJBgwZZ3ODPP/9c7mCEEEKU1czbiZeiW/Bi7+YcSM1h1cELrE28yOWCUk4b6/C2biQf64YwRLONkZrfaajOKF9HVy7Azk9Mh2/En1MaB4G9VBgTQpjoDXpSclLMI2AHMw9yufjy7S+8Q/ZW9gR7BhPmFUaYdxhBnkHYW9lXer9CVBWLEjIXl7/WGBiNRlauXImLiwvh4eEAxMXFkZubqyhxE0IIYTmVSkVYAzfCGrjxxoOt2Hk8m9Xxafx+JIOrpQ4s0vflG300ndWJDNZsJ1q9H1tVORfKn98H5/dh/O1lVC37m5Kzxt1kSqMQNUypvpSk7CTzHmDxmfEUaAsqvV8nGyfCvMJo492GMO8wWrq3xFot0w/F/cuihGzRokXmf8+YMYOhQ4eyYMECNBrTf856vZ5nn30WZ2fZ70YIISqbtUZNtxZedGvhRWGpjpgjpkqN249lmaczOnOV/po9PKLZRhv1iXL1o9KXQNJPkPQTBkcf1NemNHo2r+A7EkJUBwXaAhIyE9ifsZ8DmQdIzEqk1FB6+wvvkKe9J2HefyVg/q7+qFXqSu9XiOpC8Rqyr7/+mp07d5qTMQCNRsPUqVPp0KEDH374YYUGKIQQ4uYcbKwYEFqPAaH1uFxQyrrEi/x+JIPYkyqW6nuwVN+DJqo0Bmt28LBmJ3VU5ZtepL6aDrtmw67ZFHu3wS78SQgcBPa1K/iOhBBVJac4x1x+/kDGAZIvJ6M36m9/4R3yc/IzJWBepjVgvk6+qFSqSu9XiOpKcUKm0+lITk6mefOyfyFNTk7GYKj8MqZCCCFuzK2WDU+2b8CT7RtwpVjLtmNZxBzJYHOyFbOK6/GRbihR6iQe0WwnWr0Pu3JOabTLOABrD6Bb9zJXGvbCpf0o1P7dQSN1ooSoztIL0s3JV1xGHCfzTlZJv01rNzUnX2282+Dl4FUl/Qpxr1D8v+fo0aMZM2YMJ0+epG3btgDs2bOH999/n9GjR1d4gEIIIZRzsrM273Gm1RvYe/oyMUcyiDlSi+dzg3GikH6a3Tyi2U64+li5+rAyllL79Fo4vZZ8K3cu+w+iTufR2NYNqOC7EUIoZTQaOZt/1lx+Pi4jjrSraZXer5XKilburczTD1t7tcbFVvY7FOJWFCdkH330ET4+Pnz88cdcvGgqbVqnTh1eeuklpk2bVuEBCiGEuDPWGjVR/h5E+Xvw5oOtOHIx/8/kzIflF7rTSHWRwZrtDNLsoG45pzQ66y7hnLwQkhdy2rY5Oc2G0LjLCFw9vCv4boQQN2IwGjiec5z9GfvNo2CXii9Ver+2GltCPEPMCViwR9j0axAAACAASURBVDAO1g6V3q8Q9xPFCZlarWb69OlMnz6d/HzThqVSzEMIIe4NKpWKgLouBNR1YUrPZqTlFrHxSAYxR4KYc2ooEZimNPZV7y33lMZGJSk0Svw3JYfeJ9Y+kvzmQ2jRcSANPOWv5EJUFKPRSOqVVPZc3MPe9L3sS99XJSXonaydaO3dmjZepgQswD0Aa41UQBTiTpR7wn9WVhYpKSkAtGjRAg8PjwoLSgghRNWo52rPyA4NGdmhIXlFWramtCbmSG9mpZyls3YXj2i2EVHOKY22Kh2RxTsgYQeZ8W+wwrYbV1sOoXV4FMH1XFCrZRG/EEpkFGSwJ32POQlLL0iv9D7d7dzNFRDDvcPxd/VHI1tgCFGhFCdkBQUFTJ48mW+//dZcxEOj0TBixAg+//xzHBxkmFoIIe5FLvbW5oqNpbpQdp/qyC9HRjHr8EE6FW1ksGYH9VTlmwLlpcplSOlKSFhJwsHGfGzdjeIWg+gY3IwOTdyxtZJf8IT4p5ziHPal72Nv+l72XNzDmfwzld5nPcd6hHmHmY/6TvWlAqIQlUxxQjZ16lS2bdvGr7/+SlRUFAA7d+7kueeeY9q0acyfP7/CgxRCCFG1bKzUdG7mSedmnhgHBJCU9hDfH75A5qGNtM1fT1/1XuxV5dufKER9ihD9KUqTvmHjoTBeUHVF3awnPQLq0b25Ny4OMv1J1EwF2gLiMuLMI2DJl5MrvU9/V3/z9MM23m3wqeVT6X0KIcpSnJD99NNP/Pjjj3Tt2tX82gMPPIC9vT1Dhw6VhEwIIe4zKpWKIF8XgnxdILol5y6P4cdDJymM/5nWl9fRVl2+XxptVHoe0OzlAfaSdfxLViVHMczYBdcGofRq5U2vVt74ucmsC3H/KtGXkJCZwO6Lu9mbvpek7KRK3QdMo9LQ0q2luQBHG682uNq5Vlp/QgjLKE7ICgsL8fa+vmqWl5cXhYWFFRKUEEKI6svPzYHhXYOgaxC5ha/w+4H9aA8sI/TSb9RTZZWrTU9VHuOs1jGOdSSeb8iPZ7vw0JpIvH3q0buVN71a+RBYz1mmTol7ms6g4/Clw+y9aJqCeDDzIKWG8o00W8JGbUOwZ7A5AQv1DJUKiEJUQ4oTssjISN58802+/fZb7OzsACgqKuLtt98mMjKywgMUQghRfbk62NC7Ywfo2IESrZaDsb9hPLiEljlbsKekXG0Gqc8QpD7D/1ktZvOlNvy4tTNfbA7B08WRni1NI2ftG7tjY6Wu4LsRomJdK0V/bQri/oz9FGgLKq0/W40toV6htPNpR7hPOAHuAdhobCqtPyFExVCckM2ePZs+ffrg6+tLSEgIAAkJCdjZ2bFhw4YKD1AIIcS9wdbamtadH4LOD2Eoyif1j+WQsIz6+QfK1Z6N6v/Zu/P4qqp7//+vfYbM85yQOSRAmGWSSUDmyapgBUVbsfW21d56vT/ttZO1k9fW2mvbe+uvg8UqaCtVKoOQiAIKkdEAQiBkJmSe5+QM+/vHTk4IY4Z9yPR5Ph7rAdlnn7VXcsIh76y1P8vGMuNRlhmPUqH6sK1xNlsPz+ONz6LxdjUxb1Qwi5NDmT8qBF93ue9M9L/LS9EfLj7M0ZKjVLdWO+16RsXI+KDxTA+fzu3htzMheAKuRlenXU8I4Rw9DmTjx4/nwoULbN68mXPntPsG1q9fz4MPPoi7u7vuAxRCCDH4GNx9iF74GCx8DKpyqfnsbxhPvY13S1Gv+gtS6via6QO+ZvqAM/YYtlrv4F+nZrPjVDEmg8Lt8YEsTg5lUXIoIZ693tFFiB4raSxxVEG8FaXoRweMZkbYDKaHT2dK6BQ8zZ5OvZ4Qwvl69L+WxWJh9OjR7Nixg69//evOGpMQQoihJCAOvxXPwbIfQv5BWo+/iTHjX5hszb3qbqwhn7GGN/ieaQsf2SfzT9tcPs6azKdZFTz3/hmSw72JNhoIL6jhtthATEZZ2ij0U91SzZGSIxwpPsKRkiNOL0Uf6xPLjPAZzAifwdTQqfi7+Tv1ekKIW69HgcxsNtPS0uKssQghhBjKDAaIm4tr3Fxo/TVkvI/t880Y8z/tVXdmxcZS4zGWGo9RqXrzL9tsttru4GxxLGcxsPtPR/B2MzErIZA5icHMHRlETKCHFAYRPdLQ1sCJshNaJcTiI5yvPu/U64V5hjEjTAtg08KmSRl6IYaBHq/rePzxx3nxxRf585//jMkky0KEEEL0gqsXTHoA46QHoDofTr6Nmr4ZpSa/V90FKvVsNO1mo2k3Z+0x/NM2l122GRS3BLLnTCl7zpQCEBXgzpyRwcxNDGJWQiB+HlLwQHTVamslvSzdsQTR2aXo/V39mR4+XZsFC5tBlHeU/NJAiGGmx4nq6NGj7N27l5SUFMaPH4+nZ9e1y++++65ugxNCCDEM+MfA/O+i3PE0FKRB+hY4uw3aGnrVXbIhn2RDPj80v0m6PZ49tunssU8lR43gYlUzbx0p4K0jBRgUGB/px9yRQcxJDOK2aH+p3DgMdZSiP1x8mCPFR5xeit7T7Mm00GlMD5/O9LDpJPonYlDk+06I4azHgczPz481a9Y4YyxCCCGGM4MBYmdrbcUvIWM7pG+G3AO97nKSIYdJhhy+y9tcsI9gt30ae2xT+UKNw64qnLxYw8mLNfz+4yw8XIzcHh/InJFBzE0MYmSIl8xUDEH9VYr+9vDbmR42neTAZEwGWWEkhOjU43eEv/71r84YhxBCCNHJxRMmrtNaTQGcfFubOavO7XWXiYZLJBou8W3TNgrVIFJsU9ltm8YxdRR2DDS12fjoXBkfnSsDIMzHjTmJWjibPTKIIC8pJz4Y9Wcp+hlhM5gYMlFK0Qshbqjbgcxut/OrX/2K999/n7a2NhYuXMhzzz0npe6FEEI4l180zHsG7ngaCj7TZs3ObIO2+l53GalUOO45q1B9SLVNYY99Kofs42hD29OspK6FrccL2Xq8EIDkcB/mJmrLG6fFBuBmNury6Qn91dpr2ZGzg2PlxzhcfJjSplKnXm90wGimh2n3gUkpeiFET3U7kP385z/nxz/+MYsWLcLd3Z1XXnmFsrIyXnvtNWeOTwghhNAoCsTM1NryFyFjB5zcAjn7AbXX3QYpdaw3fcx6PqZededj+yR226axzz6JJtwc550truNscR3//4EcXE0GpscFMKf9/rMxYT4YDLK8sb80Who5UnyEg0UHSStKo6C+AD5z3vU6StFPD5vOtLBpUopeCNEn3Q5kf/vb3/i///s//u3f/g2ADz/8kJUrV/LnP/8Zg0FuRhVCCHELuXjCxPu1VnMRTrUvaazK6VO33kozdxnTuMuYRqtq5hP7OPbYp/Gh7Taq8XGc12q188mFCj65UAEfQJCXC7NHBrXffxZMmK/bDa4i+kpVVTKrMzlYdJCDlw5youwEVrvVadeTUvRCCGfqdiArKChgxYoVjo8XLVqEoigUFRURGRnplMEJIYQQN+UXpS1nnPv/QeFRbF+8R8vnW/FsK+tTt66KhUXGz1lk/BybSeGwfQx77NNIsU2lmMAu51Y0tPGv9CL+lV4EQGKIl+P+sxlxgXi6ShGHvqppqeGz4s/49NKnHCo6RHlzudOu1VGKfnrYdG4Pv11K0QshnKrb/0NYrVbc3Lr+xs9sNmOxWHQflBBCCNFjigJR07GHTebD1hmsmBqD+cJurVpj2Zk+dW1UVGYZzzLLeJbnza9fVU7/ShfKGrhQ1sBfD+ZhNircFu3P3ERt9mzcCF+Msrzxpmx2G19UfsHBS9os2BeVX2BX7U65lpSiF0L0p24HMlVV+epXv4qra2eloJaWFr7xjW902YtM9iETQgjR7xQFQsdB5GRY8CxUZsO5Hdp9Z4VH+tz95eX0M+0j2GOfxm7bNM6osUDXsGWxqRzOreJwbhUvpWTi625m9shAxwbVUQEefR7PUFHWVKYFsPZ7wera6pxynY5S9B3LEKUUvRCiP3X73ecrX/nKVcc2bNig62CEEEIIpwhMgNnf0VpdMZzfqc2c5X0Kfbz3KMlwiaQblNO/Um2zhV2nS9h1ugSA2ECP9uWNwcxMCMTHzdyn8QwmbbY2Pi/73HEvWGZ1plOuY1SMjAsax4zwGVKKXggx4HQ7kMn+Y0IIIYYEn3CY9jWtNVVB5h5t9ixrL1ib+9R1d8rpXymvsom8ygLe/KwAo0FhYqQvcxKDuSMxiIlRfpiNQ2vp3MX6i45liIdLDtPcx6/59YzyH6UFMClFL4QY4Pr1Xf7AgQOsXr2aiIgIFEVh27ZtXR7/6le/iqIoXdqyZcu6nFNVVcWDDz6Ij48Pfn5+PProozQ0NHQ559SpU8ydOxc3NzeioqL45S9/edVY3nnnHUaPHo2bmxvjx49n165d+n/CQgghBhaPAJi0HtZthmey4ctvwIT7wdW3z113lNPf5PIrjrt+g9+af8cKw2d40HLd59jsKicKavjt3gusfTWNyT9J5WuvH+NvaXnklDegqr0v799fmixNHCg8wC8O/4JV761ixbsr+Pnhn7OvcJ+uYSzALYDV8av5+ayf86zPs7y1/C2envY0d0TeIWFMCDGg9euC6cbGRiZOnMjGjRu59957r3nOsmXLuszOXX4PG8CDDz5IcXExqampWCwWHnnkER577DG2bNkCQF1dHUuWLGHRokW8+uqrnD59mo0bN+Ln58djjz0GwKFDh1i/fj0vvPACq1atYsuWLdx9992cOHGCcePGOemzF0IIMaC4eELyXVqztkHeJ9qyxnM7obFvFRuvLKd/wD6eFPvUq8rpX6mh1cqHGaV8mKFtbDzCz10rrZ8UxOyEIPw9Xfo0LmdQVZWsmizHvWDHS49jsetfAMykmJgYMpE5I+YwO2I2owJGYVAMWCwWdp2VX6oKIQaPfg1ky5cvZ/ny5Tc8x9XVlbCwa+/3kZGRwe7duzl69ChTp04F4He/+x0rVqzgpZdeIiIigs2bN9PW1sZrr72Gi4sLY8eOJT09nZdfftkRyF555RWWLVvG008/DcBPf/pTUlNT+f3vf8+rr76q42cshBBiUDC5wMiFWlv5ayg82h7OdkB1Xp+6dlUsLDaeYLHxBFaTgSP20dctp3+lSzXN/P3YRf5+7CKKAuMifB3l9afE+ONqMvZpbL1V21rL4eLDHCw6yKeXPqWsqW8B9noiPCOYPWI2s0fMZkbYDLxcvJxyHSGEuJUGfEmhffv2ERISgr+/P3feeSc/+9nPCAzU/sNKS0vDz8/PEcZA2x/NYDBw+PBh7rnnHtLS0rjjjjtwcen8LeLSpUt58cUXqa6uxt/fn7S0NJ566qku1126dOlVSygv19raSmtrq+PjujqtEpTFYpGtAPpZx9dfXofhS74HhjenvP7hU7S24DkoO4Ph/E4M53eilJ3tU7cmxd6jcvqXU1U4famW05dq+cO+bNzNBqbF+jM7IZDZCYEkhnhhcFJ5fbtqJ6Mqg0NFhzhUfIjTlaedUpLe1ejKlJApzAqfxayIWcR4x3TZD+xar7H8+x/e5PUf3gbS69+TMfQ4kDU2NnYpc+9My5Yt49577yUuLo7s7Gy+973vsXz5ctLS0jAajZSUlBASEtLlOSaTiYCAAEpKtOpVJSUlxMXFdTknNDTU8Zi/vz8lJSWOY5ef09HHtbzwwgs8//zzVx1PSUnBw0NKGA8Eqamp/T0E0c/ke2B4c+7rPx5GjMczqJTwmmOE1x4noDGrz732pJz+lZotdg5cqOTAhUoAPEwq8d5aS/BRifKEvtQHqbfXk2XN4oLlAlnWLJrUpt53dgPBhmASzYkkmhKJNcVibjZDDpzNOctZuh+A5d//8Cav//A2EF7/pqbuv0f2OJCFhoby5S9/mY0bNzJnzpyePr1H1q1b5/j7+PHjmTBhAgkJCezbt4+FCxc69do38+yzz3aZVaurqyMqKoolS5bg43P9+wGE81ksFlJTU1m8eDFm8/ApHy06yffA8HbrX/9HtOvWF2PI/ADl/E6U/IMoOpfT32Obxh7b1OuW079Sk1Xhi2qFL6q1j93NBiZF+TE1xo+pMf5MivLFw+X6PwZY7BZOlZ/iUPEh0orTOFd3rk+fz/V4mb2YHjadmeEzmRk+kwjPG88M3oz8+x/e5PUf3gbS69+xeq47ehzI3nzzTTZt2sSdd95JbGwsGzdu5OGHHyYiom9voN0RHx9PUFAQWVlZLFy4kLCwMMrKuq5Tt1qtVFVVOe47CwsLo7S0tMs5HR/f7Jzr3bsG2r1tVxYYATCbzf3+DSA08loI+R4Y3m756x8QDbf/m9acUE7/UdMHPGr6oNvl9K/UbLGTllNFWk4VACaDwrgRvkyPC2BabADTYv1ptJd3KUnfaGns07ivZ0zAGK0Yx4jZTAiegNmg/+sk//6HN3n9h7eB8Pr35Po9DmR33303d999N+Xl5bzxxhts2rSJH/7whyxdupSNGzdy1113YTI559a0wsJCKisrCQ8PB2DmzJnU1NRw/PhxpkyZAsBHH32E3W5nxowZjnO+//3vY7FYHF+Y1NRURo0ahb+/v+OcvXv38uSTTzqulZqaysyZM53yeQghhBjiOsrpT1oPbY1aKDu3A87vhtbaPnXdUU5/PR9Tr7rzsX0Su23T2GefRBNu3e7HaldJLyzndNVhXj+fidEzE6NreZ/Gdj3+rv7MGjGL2RGzmRkxkyD3IKdcRwghBqNeJ6fg4GCeeuopnnrqKX73u9/x9NNPs2vXLoKCgvjGN77Bf/3Xf930XqqGhgaysjrX3Ofm5pKenk5AQAABAQE8//zzrFmzhrCwMLKzs3nmmWcYOXIkS5cuBWDMmDEsW7aMr3/967z66qtYLBaeeOIJ1q1b55ixe+CBB3j++ed59NFH+e53v8sXX3zBK6+8wm9+8xvHdb/zne8wb948fv3rX7Ny5Urefvttjh07xh//+MfefnmEEEIIzYAqp69icCnH6HUek2cmRo9cFEPfllZei1ExMjF4oqMi4piAMRiUobXBtRBC6KXXgay0tJTXX3+dTZs2kZ+fz9q1a3n00UcpLCzkxRdf5LPPPiMlJeWGfRw7dowFCxY4Pu64J+srX/kKf/jDHzh16hSvv/46NTU1REREsGTJEn760592WSq4efNmnnjiCRYuXIjBYGDNmjX89re/dTzu6+tLSkoKjz/+OFOmTCEoKIgf/ehHjpL3ALNmzWLLli384Ac/4Hvf+x6JiYls27ZN9iATQgihr1tUTt9mUjitxnHIPo4DJJLubkT1zMHklYnBXKPP53KFMM8wZke0l6QPn4GPi9xPLYQQ3dHjQPbuu+/y17/+lT179pCcnMy3vvUtNmzYgJ+fn+OcWbNmMWbMmJv2NX/+fFRVve7je/bsuWkfAQEBjk2gr2fChAl88sknNzznvvvu47777rvp9YQQQghdGIwQfbvWlvwMSr+AjB1aQCs70+tu7cA5VxNH3Cs47PEZ51w/x6zoX/5etZugJZ5It8nMjpjF4pETmBTtj5u5f/ZCE0KIwarHgeyRRx5h3bp1HDx4kGnTpl3znIiICL7//e/3eXBCCCHEsKAoEDZeawuehcpsbdYsYwcUHrnp0xsVhYPubuzz8OCghxtVRueEIltrMLbGRKwNo7A1xYHqwjng3Plm/vLxYVyMBiZE+jItLoDpsQFMifXHx00KKwghxI30OJAVFxff9N4wd3d3nnvuuV4PSgghhBjWAhNg9ne0VlcM53dqM2d5n0J7Of0So5F9Hu7s83DniLsbFmfMgtlcsDaNxNaQhLUxCdUScMPz22x2juVXcyy/mj+QjaLAmDCfzkqOcf6EeHe/8IgQQgwH3QpkV9bRv1FdfdmDSwghhNCRTzhM+xrq1EfJKDrMvtOvs6/sGBlqi1MuN7q1jXFNBlwbY8hvnMJn9nHXKRByc6oKZ4vrOFtcx6ZDeQDEBXkyLdafabEBTI8LIDrAA8UJYVIIIQaLbgUyPz+/m75ZqqqKoijYbDZdBiaEEEIMd622Vo4UH2HfxX3sK9xHWVPfqjJei5/NxszmFuY0tzCruZkgm739kSJwSQPgjD2Gg/ZxHLKP5Yh9dI/K618pt6KR3IpG/nGsEIAQb1fHEsfpcQGMCvXGYJCAJoQYProVyD7++GNnj0MIIYQQQFVLFZ8UfsK+i/s4WHSQ5j5uKn0lg6oyobWNWc3NzGlqIbmtjZvdcTbWkM9YQz6PsROLauRzdSSH7GM5aBtHujoSS++LNlNW38rOU8XsPFUMgI+biamxAe0zaP6MH+GHi0lK5gshhq5uvYPOmzfP2eMQQgghhq3c2lxtFuziPtLL07Gr9ps/qQdCrFZmN7cwu6mZ21ta8LVfv8LxzZgVG9OV80w3nOdJ07s0qq4ctY/moH0sh+zjOKtGo9L7AFXXYuWjc2V8dE6bDXQzG5gU5cf02ACmxQVwW7Q/nq69D4BCCDHQ9OodraamhiNHjlBWVobd3vU/jYcffliXgQkhhBBDldVuJb0s3bEUMb8uX9f+zQYzt4XexpyIOcwOn8nItjaUvAOQsx/yD0Jbg27X8lRamW88yXzjSQCqVC8OtYezg/ax5KuhQO+XILZY7HyWU8VnOVUAGA0KYyN8HAFtWmwAAZ4uenwqQgjRL3ocyLZv386DDz5IQ0MDPj4+Xe4tUxRFApkQQghxDY2WRg5eOsi+i/s4cOkAta21uvbv5+rHHZF3sCBqATMjZuJp9ux6Qvh4mPk42Cxw6QTk7tcC2sXDYLfoNo4ApYFVxsOsMh4GoFAN4pBtrGMGrRy/m/RwYza7yqnCWk4V1vLnT3MBGBni5VjiODlSiosJIQaXHgey//zP/2Tjxo384he/uGn5eyGEEGI4K2kscSxFPFJyBIuOwQcg1ieWBVELmB81n4nBEzEaurH/mNEM0TO0Nu8ZaGuCgjTI2aeFtOJTQO+XNF4pUqngy6b9fJn9AGTaRzgKhHxmT6aevv8skVXWQFZZA28dKQDAz8XIB3UnmRztz6QoP8aN8JVljkKIAavH706XLl3i3//93yWMCSGEEFdQVZWzVWfZd3Ef+y/uJ6MqQ9f+DYqBySGTmR85n3lR84jzjet7py4eMHKh1gCaqiDvE232LHc/VGb1/RqXSTJcIslwiUfYg01VOKUmcNA+loP2cZywJ9JK35cf1rQp7D5Tyu4zpQAYFEgK9WZipB+Tov2YGOlHUqgXJqMUCxFC9L8eB7KlS5dy7Ngx4uPjnTEeIYQQYlBxdml6D5MHs0fMZkHUAuaOmIufW9+W/N38ggGQ/CWtAdQWQu6BzoBWX6zbpYyKymQli8mGLJ7gX7SoZo7Zkxz3n51W47H3oUBIB7sK50rqOVdSz9+PXQS0YiHjR/gyKcqPiVFaSIv0d5c90YQQt1y3Atn777/v+PvKlSt5+umnOXv2LOPHj8dsNnc596677tJ3hEIIIcQA4+zS9KEeocyPms+CqAVMC5uGi7Efi1b4RsKkB7SmqlCR2RnO8j6BFv3uhXNTLMwxnmGO8QwA9XhwyJbsmEHLViPoS4GQy7VY7BzNq+ZoXrXjWKCnCxOj/C4Lab74eUjBECGEc3UrkN19991XHfvJT35y1THZGFoIIcRQ5ezS9GMCxjjuBxsdMHpgztQoCgSP0tqMx8Bug+L0zoBW8BlYW3S7nDdNLDUeY6nxGAAV+HPANtaxB1oxgbpdC6Cysa1LyX2A2ECPzoAW5UdyuA9u5m7cqyeEEN3UrUB2ZWl7IYQQYqi7FaXpZ4TPYEHUAu6IvIMwzzBd+78lDEYYMUVrc58CSwsUHtEKhOTsh6IToGNwDaKae42fcq/xUzBDoSGCfW1j+NQ+njR7MrV46XatDnmVTeRVNrEtvQgAs1FhTLgPEyP92mfTfIkP8sJgGIABWggxKPT4HrK//e1v3H///bi6unY53tbWxttvvy1l74UQQgxa/V6afrAzu0HcHVpbiLacMe9gZ4n9cn2LnETai9hgKmIDe1FRyDbG81HbGA7Zkkm3J1CDt67XA7DYOsvuv/GZFtK9XU1MiPK9LKT5Eerjpvu1hRBDU48D2SOPPMKyZcsICQnpcry+vp5HHnlEApkQQohBZUCWph8q3Hxh9AqtAdSXagVCcvdpAa32om6XUlAZactmpDGbx4w7AKgwR3DSHs9nLdGcsifwhRpLI+66XbNDfauVg1mVHMyqdBwL93VzBLSJUb5MiPTDS0rvCyGuocfvDKqqXnNde2FhIb6+vroMSgghhHCWy0vT77u4j3NV53Tt3yml6YcK71CYcJ/WVBWqcjpnz3IPQHOVrpcLshSxkCIWttcfU1EoMkdzwhrL0bZYTtkTyFCjdSm1f6Xi2haKa0vYfaYE0G6/Swzx6jKLNirMG7OU3hdi2Ot2IJs8eTKKoqAoCgsXLsRk6nyqzWYjNzeXZcuWOWWQQgghRF8MudL0Q4GiQGCC1qZuBLsdSr/oDGj5h8DSqO8lURlhyWcE+aw2axtV2xQjecY4jrbF8LktnlP2eDLVSGzoO5OpqpBZ2kBmaQPvHC8EwNVkYNyIjqWOvkyO8icqQErvCzHcdDuQdVRaTE9PZ+nSpXh5dd446+LiQmxsLGvWrNF/hEIIIUQvVLdUk5afNjxK0w8FBgOET9DarG+DtQ0uHdcKhOTuh8KjYLfqflmjaiPBmkWCIYt1hr0AtCmuZBniONwaw0l7AqfUeHLVMFQd9kS7XKvVzvH8ao7nd5be9/cwO/ZF69jEOsBTvreEGMq6Hciee+45AGJjY7n//vtxc5ObVYUQQgwsebV5fJj3Idvqt/Gj936ke2n65MBk5kfNZ37kAC5NP1SYXCBmptYWPAutDVCQ1hnQSk477dIuaivJtnMkmzqXszYbPDmrxnHUEsdJuzaTdokg9NoXrUN1k4V958vZd77ccSw6wMOxL9rkaD/GRvhK6X0hhpAe30P2la98xfkTFAAAIABJREFUBdCqKpaVlV1VEj86OlqfkQkhhBDdkFOTw578PaTkpZBVk6Vr30OiNP1Q4eoFiYu1BtBYCXkHtOWNOfugOtepl3e3NzKFL5hi+sJxrNbgyyl7PCesHSEtgQr0v5++oKqJgqomtp/USu+bDAqjwrwd96JNivIjIdgLo5TeF2JQ6nEgu3DhAhs3buTQoUNdjncU+5CNoYUQQjhbVnUWqfmppOTrH8KGfGn6ocIzEMbeozWAmgLI2Y89P4368wfwab2EovMM6ZV87bXM5XPmmj53HKswBHHCGke6LZ6Tajyn7XHU6bw/mtWucqaojjNFdWw5XACAp4uR8ZG+WkiL1GbR5H40IQaHHgeyr371q5hMJnbs2EF4eLj8QxdCCOF0qqqSVZNFSn4KKXkp5NTm6Np/nG8c8yPnD8/S9EOFXzTc9hC28evYZ9jFisXzMVdkwKUT2gbVRZ9Dpb7h/VqC7BUsMVSwxHDUcaxQCee4NY5T9nhO2uM5o8bSjL63fjS22fgsp4rPcjorVXq7mhgT7kNyRHsL9yEp1BsXk1R2FGIg6XEgS09P5/jx44wePdoZ4xFCCCEALYRlVmc6ZsJya/VbktZRmn5B1ALmRc4j1jdWt77FAGH2gOjbtdahuQaK09tD2uda03EvtOuJVIuJNBbzJaO2usiOgRwiOWGN45Qaz0l7AufVKNow63rd+lYrR/KqOJLXGdLMRoWRId4kh/swtj2ojQn3wddd32sLIbqvx4EsOTmZiooKZ4xFCCHEMNcRwvbk7SE1P5W8ujzd+pbS9AJ3P4ifr7UODWVaMHOEtBPQWH7t5+vEgJ2RFDDSVMCX0crvWzBzTo0i3RbPKVUrGnJBjcSuc2VHi00lo7iOjOI6/nmi83hUgDvJ4T4kh/uSHKGFtXBfN1kJJcQt0ONA9uKLL/LMM8/wi1/8gvHjx2M2d/2Nio+Pj26DE0IIMfSpqsq5qnOk5KeQmp9Kfl2+bn1LaXpxU14hkLRUa6BtGFZbqAUzR0hLh9Zapw7DjIXxSg7jTZ3LcVtw5bQ91lEw5JQaR54aht6VHQEuVjVzsaqZPWdKHcf8PMxdZtKSw31JCPbEJJtZC6GrHgeyRYsWAbBw4cIux6WohxBCiO5SVZWMqgxS8rQQVlBfoFvf8b7xxLTE8PUFX2dcyDj5Db/oGUUBvyitJX9JO2a3Q1VO571ol05A8UnQeW+7K7nRyjTDeaYZzjuO1eNJui2ufRYtgVP2eIoJwBkhrabJwqHsSg5lVzqOuZgMjA7ruuRxdJgPnq49/pFSCNGux/96Pv74Y2eMQwghxBCnqipnK8+yJ38PqXmpFDYU6tb3SL+RLIldwpKYJUR7RrNr1y7ZJ0zox2CAoJFam/Bl7ZjNCuXnuoa00jNgtzh1KN40Mtf4BXPpLL9fga+21LF9Fu2MPY5yfHFGSGuz2jlVWMupws4ZQ0WBuEBPxrQXDulY8hjiLXvWCtEdPQ5k8+bNc8Y4hBBCDEGqqnKm8gwpeSmk5KdwqeGSbn0n+ieyJGYJS2KXEO8b7zhusTj3B2IhADCaIGyc1m57WDtmadFC2eUhrfwcoDp1KEHUssj4OYuMneX36/Agxx5OjhpOrj2MHDWCXDWMXDVM9wqPqgo5FY3kVDSy81Rx57i8XB3hrCOoxQV6YpD90oToolfzyzU1NfzlL38hIyMDgLFjx7Jx40Z8ffXfDFEIIcTgoqoqpytOO5YjFjUW6db3KP9RLIldwuKYxcT5xunWrxC6MLtB5BStdWht0JY3dhQMuXTC6ZtYA/jQxCRDNpPIhit2cShSA8ixh5Ortge29j8L1WBdi4hUNLRyILOcA5mdRVI8XIzakscIH8ZG+JIc7sOoMG/czLLVhBi+ehzIjh07xtKlS3F3d2f69OkAvPzyy/z85z8nJSWF2267TfdBCiGEGNjsqp1T5adIzU8lNT+V4sbimz+pm0YHjGZJjBbCpDy9GHRcvSB2ttY6NFV1Lb9/6QTU6/eLi5uJUKqIMFYxhzNdjreqJgrUUEdIy75sdq0Kb/RYAtnUZuNEQQ0nCmocx4wGhYRgz8uWO2pBzd9TivCI4aHHgew//uM/uOuuu/jTn/6EyaQ93Wq18rWvfY0nn3ySAwcO6D5IIYQQA09HCOsoUV/aVHrzJ3XTmIAxnfeE+UTr1q8QA4JHACTcqbUO9SWXld9vn0lrrrp+H07gqlhJVC6RyNVLi2tVj8tCWufMWq4aRguufbquza6SWdpAZmkD29I7g2m4r9tVVR6jAtzl3lAx5PRqhuzyMAZgMpl45plnmDp1qq6DE0IIMbDYVTvpZemOzZrLmsp06zs5MFm7JyxmCVE+Ubr1K8Sg4B0Go5ZrDbQbs2oKri6/31bfL8PzVZqYpFx7CeQlNdCxBDJX1WbUctQwLvVxCWRxbQvFtS3sPdf5PuPtanIUD+kIaokh3riYpBS/GLx6HMh8fHwoKChg9OjRXY5fvHgRb29v3QYmhBBiYLCrdj4v+5yUvBQ+zP+Qsmb9Qti4wHGOe8IivSN161eIQU9RwD9Ga2Pv0Y7Z7VCZdVlIOwElp8Ha0q9DHaFUMsJY2aXyI2hLIPPVUEdBkRw13BHcersEsr7VypHcKo7kds4emo0KiSHafWmjQj2prVWoamwj1M98g56EGDh6HMjuv/9+Hn30UV566SVmzZoFwMGDB3n66adZv3697gMUQghx69nsNk6UnSAlL4W9BXspby6/+ZO6aXzQeO2esNjFjPAaoVu/Qgx5BgMEJ2lt4jrtmM0CZRmdIa3kNFRmO30j6+5wVawkKZdIusYSyBrV01FMJOeKJZCt9OzeMYtN5WxxHWeL69qPGPn92X0EebmQGOLNqDBvEkO9SAr1JinEG18PCWpiYOlxIHvppZdQFIWHH34Yq9UKgNls5pvf/Cb//d//rfsAhRBC3BodIWxP3h72FuylorlCt74nBE9wFOaI8IrQrV8hhj2jGcInaG3KV7VjqgqNFVB5QZtRq7ighbTKC1CV6/S90rrDT2lkspLFZLKuWgJZqAa1FxPprACZo4ZTpAb1aAlkRUMbFQ2VpOVUdjke6uNKUqg3iSHeJIV6kRiq/entJkFN9I8eBzIXFxdeeeUVXnjhBbKzswFISEjAw8ND98EJIYRwLqvdyvHS49pyxIIPqWrRr4jAxOCJjhAW7hWuW79CiJtQFPAK1lrMrK6P2axQWwAVWVpYc4S2rFta6fFGIpUKIo0V11gCaSbvsiqQuWoY2XZtOWQ1Pt3uv7SuldK6Vj650PWXThG+bo5wlhjqzahQb0aGeOHp2qtdooTotl5/h3l4eDB+/Hg9xyKEEOIWsNqtHCs95liOqGcImxwymSUxS1gUs4gwzzDd+hVC6MRogoB4rbGk62OtDVCV3R7Usttn1tqDW2vdNbu7lVwVC6OUQkZReNVj1aqXo6BIgT2EMvwoV30pV/0oV/2owBfLTX7sLaptoai2hf2ZXZdoR/q7a8sd28NaUntQk73ThF66Hcg2btzYrfNee+21Xg9GCCGEc1jtVo6UHCElL4WPCj6iurVal34VFC2ExS5hUfQiQj1DdelXCNEPXL0gfKLWLqeq0FjeNaB1tAGyBNJfacBfyeK2ayyB7FCtejlCmhbYLgtt+Do+rsEL9bKlkYXVzRRWN/PRZdUeFQWiAzy6hLTEEG/igz0lqIke63Yg27RpEzExMUyePBlVVZ05JiGEEDqw2C0cLT5KSr42E1bTWnPzJ3WDgsJtobc5ZsJCPEJ06VcIMUApCniFaO3yDa5BWwJZk98Z0ByhLXvALIHsoIW2hmsWGbmcRTVSgS/lqi9l7TNsnYGtPcThR1mlL/mVTaSe7dyD0aBAbJAnSZfdnzYqzJvYQE8pzS+uq9uB7Jvf/CZvvfUWubm5PPLII2zYsIGAgABnjk0IIUQPWewWDhcf1mbCLn5ErU6V1hQUpoROccyEBXsE69KvEGKQM5ogMEFrLO36WMcSyMuLinTcr9ZP+6l1h1mxEU4V4crNl3M3qG6dAa0jsFX5Ul7lx6kMX/a2H6s1+BId5KPNpIV6MSrUm8RQb2IDPTAZJagNd90OZP/7v//Lyy+/zLvvvstrr73Gs88+y8qVK3n00UdZsmSJ7JouhBD9RFVVTpafZHv2dvbk79EthBkUA1NDp7IkZgkLYxYS5B6kS79CiGHiRksgG8quLipSmQXVuWC39s94e8FLacFLaSGO0hueZ1cVqmq9Ka/xo/ycFuA+VP2oUvwweIfiETiCwNBIwkbEEB85guhAT4wG+dl6uOhRUQ9XV1fWr1/P+vXryc/PZ9OmTXzrW9/CarVy5swZvLy8nDVOIYQQV8ivy2dHzg52ZO+gsOHqm9x7w6AYmBY6jSWxS7gz+k4JYUII/SkKeIdq7UZLIK+8Z62+uH/GqwODohJEHUFKHWOufLCpvV0EjmnVJIvxpcEUiMU9GIN3KO7+4fiFROIXEonBO6x9CWkomN1u+eci9NfrKosGgwFFUVBVFZvNpueYhBBCXEd1SzV78vawPWc7p8pP6dKnUTEyLUwLYQujFxLgJsvRhRD95PIlkElXLoGsb1/6mHXFPWvZA3oJZE+5KhYiqQBbBTSchwagGDh79bkWszd4hWHyDUXxCtVCmrs/mN3bmweY3LQ/Oz42X/6xO5jcweSqBWXRL3oUyFpbWx1LFj/99FNWrVrF73//e5YtW4bBIOtfhRDCGVptrey/uJ/tOdv5tPBTrGrfl/MYFSPTw6Y7Qpi/m78OIxVCCCdy9YaISVq7nGMJ5AWs5Re4cOITkiL8MDaVa8cbSrVmaeqfcTuR2VIP1fVQfaGPPSldQ9p1A90Vj3U5x/0a53l0hj6zu3aeZIardDuQfetb3+Ltt98mKiqKjRs38tZbbxEUJEtZhBDCGeyqnROlJ9iRs4OUvBTqLX3/7a9JMTEjfIa2HDHqTvzc/HQYqRBC9LPLlkCqI2aQWRTAyGUrMJrNXc9rre8a0K759zKtqcNt9ZcKlkatOZvJ/dqzdNcMdNcIdY5z3K4OhYoZo60F7DbAfNOhDBTdDmSvvvoq0dHRxMfHs3//fvbv33/N8959913dBieEEMNNTm0OO7J3sDNnJ0WNfS8ZbVJM3B5xO0titHvCfF19dRilEEIMQq7eWgtMuPF5djs0VV4nrF1xrEWf7USGFWuz1pr12Q/zcmZgFcCpx8DoAvOegTue1v06eut2IHv44YelkqIQQjhBZXMlu/N2sz17O2cqz/S5P4NiYGb4TJbFLWNB1AIJYUII0RMGA3gFa41xNz7X0gKNZTeYbdOOqfWlKLbWWzJ80c7WBsrgWB7Zo42hhRBC6KPZ2sy+i/vYnr2dQ0WHsOmwPGZMwBhWJ6xmedxyqY4ohBC3gtkN/KK1dgOKqkJL7VXBrbW2mPqKS1hqSlAay3BrrcDHXoMB9RZ9AkOc2aO/R9Atva6yKIQQomfsqp2jJUfZnr2dDws+pFGHtfphnmGsil/FqvhVJPjdZBmOEEKI/qEo4O6nteAkx2HX9taFzUptZREXC/IpK86ntvwSrdXFqA2leFurCFZqCKKWEKUGb6X5Vn4Wg4/Zvb9H0C0SyIQQwskuVF9ge852duXsorTpxpuHdoeX2YvFMYtZnbCaKaFTMAySJRlCCCG6wWjCNyQa35BoYG6XhyobWsksbeBgWT2ZpfXkF1VQVV6Ia0sFwUoNwUotnrTgrrTihgU3WnGnDXdF+9P1sr+704qrYsG9/RwPZQguqZQZMiGEGL7Km8rZlbuLHTk7OFd1rs/9mRQTs0fMZlXCKuZHzsfNJJuBCiHEcBPo5cpML1dmJgQ6jqmqSnlDKxdKG8gsrSe3opEz5Y3klDdQVNvSg95VXLHg1h7W3BXtTzfacGv/e0e4c8XS5WM32rTnXSfsac9pczxmVG7RkkyZIRNCiOGlydLE3oK97MjZwWfFn2FX7X3uc3zQeFbFr2JZ3DLZsFkIIcRVFEUhxNuNEG83Zo/sev9wU5uV3IpGcsq1llvRQE77xw2tV+5pqdCKC624UIsXXW5j0zU/qbhgxQ1tFu/yEOemaMHOQ2llhCeM8IJwDwhxtxPkasPfbMVDsaBYm7V95SzNXZpqaaK1oQZXox2lrUkrlT8ISCATQog+sNltHC4+zPac7ewt2Euzte/r+Ud4jWBV/CpWxq8kzjdOh1EKIYQYjjxcTIyN8GVsRNdqu6qqUl7fSnZ5IzkVDeSWN7YHtQYuVjdjsztzBkuhDTNtmKmD6we/uvZ2BU8XI3HBnsQHeREf5kl8sBfxQZ7EBXniYlDZs2sXK1aswGw2a5uGDwISyIQQoodUVeV89Xm2Z2/ng9wPKG8u73Of3i7eLI1dyur41UwOmSzbjAghhHAaRVEI8XEjxMety/JHgDarnYKqRrLLG9tn1xq0GbaKRqoa2/ppxJ0a22x8camOLy5dndZCfVzxwcBh21lGhvgwOtybWQkDv+pwvwayAwcO8Ktf/Yrjx49TXFzMe++9x9133+14XFVVnnvuOf70pz9RU1PD7Nmz+cMf/kBiYqLjnKqqKr797W+zfft2DAYDa9as4ZVXXsHLy8txzqlTp3j88cc5evQowcHBfPvb3+aZZ57pMpZ33nmHH/7wh+Tl5ZGYmMiLL77IihUrnP9FEEIMGiWNJezK3cX27O1k1WT1uT+TwcQdI+5gdcJq7oi8Axejiw6jFEIIIXrPxWRgZIg3I0O8r3qspqlNm1Urb+hcClnRQF5FE222vi/T76vSulZKMXDhSCEAU2L8mfVNCWQ31NjYyMSJE9m4cSP33nvvVY//8pe/5Le//S2vv/46cXFx/PCHP2Tp0qWcPXsWNzfthvYHH3yQ4uJiUlNTsVgsPPLIIzz22GNs2bIFgLq6OpYsWcKiRYt49dVXOX36NBs3bsTPz4/HHnsMgEOHDrF+/XpeeOEFVq1axZYtW7j77rs5ceIE48bdZENAIcSQ1mhpJDU/lR3ZOzhScgRVh4X0k4InsTphNUtiluDn5qfDKIUQQgjn8/NwYUqMC1Ni/Lsct9lVLlU3k+1Y/tjguG+tpK4nhUX0FR/k2W/X7ol+DWTLly9n+fLl13xMVVX+53/+hx/84Ad86UtfAuBvf/sboaGhbNu2jXXr1pGRkcHu3bs5evQoU6dOBeB3v/sdK1as4KWXXiIiIoLNmzfT1tbGa6+9houLC2PHjiU9PZ2XX37ZEcheeeUVli1bxtNPPw3AT3/6U1JTU/n973/Pq6++egu+EkKIgcRqt3Ko6BA7snfw8cWPabH1/T+TaO9ox35hUT5ROoxSCCGEGBiMBoXoQA+iAz1YMKrrY42t7YVFuix/1IJbY5vNqeOKD/a6+UkDwIC9hyw3N5eSkhIWLVrkOObr68uMGTNIS0tj3bp1pKWl4efn5whjAIsWLcJgMHD48GHuuece0tLSuOOOO3Bx6VwKtHTpUl588UWqq6vx9/cnLS2Np556qsv1ly5dyrZt2647vtbWVlpbO/drqKvT1rFaLBYsFkufP3/Rex1ff3kdhq/efA+oqkpGVQY7c3eyp2APVS1VfR6Hn6sfi6MXszJuJeMDxzvuC5PvTeeS94DhTV7/4U1e/4HHxQCjQjwYFeIBBDuOq6pKaX0reRVN5FRo96vltv/9Uk0zetQVifF367fvhZ5cd8AGspKSEgBCQ0O7HA8NDXU8VlJSQkhISJfHTSYTAQEBXc6Ji4u7qo+Ox/z9/SkpKbnhda7lhRde4Pnnn7/qeEpKCh4eg2MTuqEuNTW1v4cg+ll3vgeq7dWcbDvJybaTlNv7XpzDhIlR5lFMcplEoikRU7mJwvJCCinsc9+iZ+Q9YHiT1394k9d/cPEDJgOTg4AgsNqhvAXKmhXKOv5s/3uTtftFry6ePcauPCcN+iaampq6fe6ADWQD3bPPPttlVq2uro6oqCiWLFmCj49PP45MWCwWUlNTWbx4sVbyVAw7N/seqG+r58OCD9mZt5MTZSd0ueZtIbexInYFi6MX4+1y9Y3Q4taR94DhTV7/4U1e/6GvuqnNMZN2+axaQVUTFlvntJpBgQ13L8PFZOiXcXasnuuOARvIwsLCACgtLSU8PNxxvLS0lEmTJjnOKSsr6/I8q9VKVVWV4/lhYWGUlpZ2Oafj45ud0/H4tbi6uuLq6nrVcbPZLG8AA4S8FuLy7wGLzcKnlz5le8529l/cT5u976V7Y31iWZ2wmpXxKxnhNaLP/Ql9yXvA8Cav//Amr//QFeJrJsTXkxkJwV2OW2128irq+ccH+wmKS6a2xYqn+9U/q98qPfn+G7CBLC4ujrCwMPbu3esIYHV1dRw+fJhvfvObAMycOZOamhqOHz/OlClTAPjoo4+w2+3MmDHDcc73v/99LBaL4wuTmprKqFGj8Pf3d5yzd+9ennzyScf1U1NTmTlz5i37fIUQ+lNVlZPlJ9mevZ09eXuoaa3pc58BbgEsj1vO6vjVJAcmy35hQgghxABgMhqICfBgrL/KilkxgyqQ92sga2hoICurcy+f3Nxc0tPTCQgIIDo6mieffJKf/exnJCYmOsreR0REOPYqGzNmDMuWLePrX/86r776KhaLhSeeeIJ169YREREBwAMPPMDzzz/Po48+yne/+12++OILXnnlFX7zm984rvud73yHefPm8etf/5qVK1fy9ttvc+zYMf74xz/e2i+IEEIXF+sv8lHLR/xxxx8pqC/oc3+uRlfujLqTVQmrmBkxE7Nh8LzJCyGEEGJg69dAduzYMRYsWOD4uOOerK985Sts2rSJZ555hsbGRh577DFqamqYM2cOu3fvduxBBrB582aeeOIJFi5c6NgY+re//a3jcV9fX1JSUnj88ceZMmUKQUFB/OhHP3KUvAeYNWsWW7Zs4Qc/+AHf+973SExMZNu2bbIHmRCDSJOliT15e3gv6z0+L/tcO9iHavUKCtPDprMyfiWLYxbj5TI4SucKIYQQYnDp10A2f/58VPX6NS0VReEnP/kJP/nJT657TkBAgGMT6OuZMGECn3zyyQ3Pue+++7jvvvtuPGAhxIBzvuo872S+w86cnTRYGvrc30i/kayKX8XK+JWEeV7/PlIhhBBCCD0M2HvIhBDieposTXyQ+wH/vPBPTlec7nN/Qe5BrIhbweqE1YzyHyX3hQkhhBDilpFAJoQYNM5WnmVr5lZ25uykydr9/T2uxd3kzsLohayOX8308OmYDPJ2KIQQQohbT34CEUIMaA1tDezK3cXWzK1kVGX0qS+DYmBG2AxWJ6xmYfRCPMyyibsQQggh+pcEMiHEgKOqKmcqz7A1cyu7cnfRbG3uU3+j/EexOmE1y+OWE+IRotMohRBCCCH6TgKZEGLAqG+rZ2fOTrZmbuV89fk+9eWteHPv6Hu5K/EukvyTdBqhEEIIIYS+JJAJIfqVqqqcqjjF1syt7Mnb06fZMINiYM6IOdwTfw91J+tYPXn1oNoYUgghhBDDjwQyIUS/qG2tZUfODrZmbiWrJuvmT7iBUI9Q1iSu4Z7EewjzDMNisbDr1C6dRiqEEEII4TwSyIQQt4yqqqSXpztmw1ptrb3uy6gYmRs5l/uS7mN2xGyMBqOOIxVCCCGEuDUkkAkhnK62tZb3s99na+ZWcmpz+tRXhGcE9ybey90j7ybUM1SnEQohhBBC9A8JZEIIp1BVleOlx9l6YSupeam02dt63ZdRMbIgagFrktYwM3ymzIYJIYQQYsiQQCaE0FV1S7VjNiyvLq9PfY3wGsHapLV8KeFLBHsE6zNAIYQQQogBRAKZEKLPVFXlaMlRtmZu5cOCD7HYLb3uy6SYuDP6TtYmrWVG+AwMikHHkQohhBBCDCwSyIQQvVbZXMm/sv/FPzP/SUF9QZ/6ivaOZk3SGu5KuIsg9yCdRiiEEEIIMbBJIBNC9IhdtXO4+DBbM7fy0cWPsNqtve7LbDCzKHoRa5PWMjVsqsyGCSGEEGLYkUAmhOiWiuYKtmVt45+Z/6SwobBPfcX6xLI2aS2rE1YT4Bag0wiFEEIIIQYfCWRCiOuyq3bSitLYmrmVfRf3YVV7PxvmYnBhcexi1iauZUroFBRF0XGkQgghhBCDkwQyIcRVyprKeO/Ce7x74V2KGov61Fe8bzz3Jd3H6oTV+Lr66jRCIYQQQoihQQKZEAIAm93GwaKDbM3cyoHCA9hUW6/7cjW6sjR2KWuT1jIpeJLMhgkhhBBCXIcEMiGGuZLGEm02LOtdShpL+tTXSL+RrE1ay6r4VTIbJoQQQgjRDRLIhBiGrHYrn176lK2ZW/nk0ifYVXuv+3IzurEsbhlrk9YyIWiCzIYJIYQQQvSABDIhhpGihiLevfAu7114j7Lmsj71Ncp/FPcl3ceK+BV4u3jrNEIhhBBCiOFFApkQQ5zFbuHAxQNsvbCVg5cOoqL2ui93kzsr4lawNmktYwPHymyYEEIIIUQfSSATYogqrC/UZsOy3qOiuaJPfSUHJrM2aS0r4lbgafbUaYRCCCGEEEICmRBDiMVuYd/FfWzN3EpaUVqfZsM8zZ6sjFvJmqQ1JAcm6zhKIYQQQgjRQQKZEENAbWstWzO38ta5tyhtKu1TX+ODxrM2aS3LYpfhYfbQaYRCCCGEEOJaJJAJMYjl1OawJWML72e/T7O1udf9eJm9WBW/irVJaxkVMErHEQohhBBCiBuRQCbEIKOqKmnFabxx9g0+vfRpn/qaGDyRtUlrWRKzRGbDhBBCCCH6gQQyIQaJFmsLO3J28ObZN8muze51P94u3tyVcBdrEteQ6J+o4wiFEEIIIURPSSATYoArayrj7XNv807mO9S01vS6n9tCbmNt0loWxyzGzeSm4wiFEEIIIURvSSATYoA6U3GGNzLeYE/uHqyqtVd9+Lr6clfCXaxNXEu8X7zOIxRCCCGEEH0lgUyIAcSxutd7AAAdWElEQVRqt/LxxY954+wbfF72ea/7GRMwhg3JG1gauxRXo6uOIxRCCCGEEHqSQCbEAFDXVsd7F95jS8YWihqLetWHgsKd0XfyUPJD3BZyG4qi6DxKIYQQQgihNwlkQvSj/Lp8NmdsZlvWtl6Xrfc0e3Jv4r2sH72eKO8onUcohBBCCCGcSQKZELeYqqocKTnCm2ffZH/hflTUXvUT6RXJg2Me5O6Rd+Pl4qXzKIUQQgghxK0ggUyIW6TV1squnF28mfEmmdWZve5nauhUHkp+iHmR8zAajDqOUAghhBBC3GoSyIRwsormCv5+/u/84/w/qGqp6lUfZoOZ5XHL2TBmA2MCx+g8QiGEEEII0V8kkAnhJBmVGbyZ8Sa7cndhtfeubH2AWwD3j7qfL4/6MkHuQTqPUAghhBBC9DcJZELoyGa3sa9wH2+efZNjpcd63U+SfxIbxmxgRfwKKVsvhBBCCDGESSATQgcNbQ28l/UemzM2c6nhUq/6UFCYFzmPh5IfYlrYNClbL4QQQggxDEggE6IPLtZfZEvGFt7Leo9GS2Ov+nA3uXPPyHt4YMwDxPjE6DxCIYQQQggxkEkgE6KHVFXleOlx3jj7Bh9f/LjXZesjPCN4YMwD3JN4Dz4uPjqPUgghhBBCDAYSyITopjZbG7vzdvPm2TfJqMrodT+TQybzUPJDLIhagMkg/wSFEEIIIYYz+WlQiJuobK7kH5n/4O/n/k5lS2Wv+jApJpbGLWXDmA2MCxqn8wiFEEIIIcRgJYFMiOs4X3WezRmb2ZmzkzZ7W6/68HP1476k+1g3eh0hHiE6j1AIIYQQQgx2EsiEuIxdtfNJ4Se8cfYNDpcc7nU/Cb4JbEjewMr4lbib3HUcoRBCCCGEGEokkAkBNFma2Ja1jc0ZmymoL+h1P3NGzOGh5IeYGT5TytYLIYQQQoibkkAmhrWihiK2ZGzh3QvvUm+p71Uf7iZ37kq4iwfGPEC8b7zOIxRCCCGEEEOZBDIx7KiqSnp5Om+cfYO9BXuxq/Ze9RPqEcr60etZm7QWX1dfnUcphBBCCCGGAwlkYtiw2Cyk5Kfwxtk3OFN5ptf9TAiawEPJD7EwZiFmg1nHEQohhBBCiOFGApkY8qpbqtmauZW3z71NWXNZr/owKkYWxyxmQ/IGJgZP1HmEQgghhBBiuJJAJoasnNoc3sp8ix05O2i1tfaqDx8XH9YmrWX96PWEeYbpPEIhhBBCCDHcSSATQ4pdtXOw6CCbGjaRtTOr1/3E+sSyYcwGViesxsPsoeMIhRBCCCGE6CSBTAwJNruN1PxU/nj6j1yovtDrfmZFzGLDmA3MHjEbg2LQcYRCCCGEEEJcTQKZGNSsdiu7cnfxp1N/Iq8ur1d9uBpdWRW/ig1jNjDSf6S+AxRCCCGEEOIGJJCJQanN1sb72e/z59N/5lLDpV71EewezLrR67gv6T783fx1HqEQQgghhBA3N6DXZP34xz9GUZQubfTo0Y7HW1paePzxxwkMDMTLy4s1a9ZQWlrapY+CggJWrlyJh4cHISEhPP3001it1i7n7Nu3j9tuuw1XV1dGjhzJpk2bbsWnJ3qhxdrC5ozNrHh3Bc+nPd+rMJYcmMwLc19gz5o9PDbhMQljQgghhBCi3wz4GbKxY8fy4YcfOj42mTqH/B//8R/s3LmTd955B19fX5544gnuvfdeDh48CIDNZmPlypWEhYVx6NAhiouLefjhhzGbzfziF78AIDc3l5UrV/KNb3yDzZs3s3fvXr72ta8RHh7O0qVLb+0nK66rydLEP87/g01nNlHZUtnj5xsUAwujF7JhzAYmh0xGURQnjFIIIYQQQoieGfCBzGQyERZ2dbnx2tpa/vKXv7BlyxbuvPPO/9fevUdFXSZ+HP8Md+Qu6gghSgoCCgrRKrqZuynmCuX+TI+uoUmieeiCxa7dbG0rzU738hJaYmW5rkfLNcvMSNMl0wiB9IdoCqWi6w1EFEeY3x8d5xeJJAZ8kXm/zplz5Pt9npkPPOOBz3lmviNJWrJkiSIiIvTVV1+pf//++vTTT7Vr1y599tlnMpvN6tu3r5566inNmDFDs2bNkouLixYuXKiQkBC98MILkqSIiAht2bJFL730EoWsFTh9/rTe/9/39c6ud3Sq+lSj53s6e2pU6CiNixin6zyva4aEAAAAwNVr9YWsuLhYgYGBcnNzU3x8vObMmaPg4GB98803slgsGjJkiG1seHi4goODlZOTo/79+ysnJ0dRUVEym822McOGDdO0adP03XffKSYmRjk5OXXu4+KY9PT0BnNVV1eruvr/P9uqoqJCkmSxWGSxWJriW7drp6pP6f2i9/V+0fuqtFQ2er6nyVMpUSka3XO0PJw9JIl1sSMX15o1t0+sv31j/e0b62/fWtP6NyZDqy5k/fr1U1ZWlnr27KnDhw/rySef1E033aTCwkKVlZXJxcVFvr6+deaYzWaVlZVJksrKyuqUsYvnL55raExFRYXOnj0rd3f3erPNmTNHTz755CXHP/30U7Vrx+dWXa3K2kptrd6qbdXbdF7nGz3fx+SjQW6DFOsSK+dSZ20q3dQMKXGt2LBhg9ERYCDW376x/vaN9bdvrWH9q6qqrnhsqy5kw4cPt/07Ojpa/fr1U9euXbVixYrLFqWW8sgjj+jBBx+0fV1RUaEuXbooISFB3t7eBia7Nh2tOqqlu5dq9d7VOldzrtHzgzyDlNIrRSO6jZBqf/qPOHToUDk7OzdDWrR2FouF54AdY/3tG+tv31h/+9aa1v/iq+euRKsuZL/k6+ursLAw7d27V0OHDtX58+d16tSpOrtkR44csb3nrHPnzvr666/r3MfFqzD+fMwvr8x45MgReXt7N1j6XF1d5erqeslxZ2dnw58A15KDlQf1ZsGb+mDvB7LUNn57OcQnRKlRqRoeMlxODj89nS9uEbMW4Dlg31h/+8b62zfW3761hvVvzOO36sve/1JlZaX27dungIAA3XDDDXJ2dtbGjRtt54uKilRaWqr4+HhJUnx8vAoKCnT06FHbmA0bNsjb21uRkZG2MT+/j4tjLt4HmseB8gN6fMvjGrFqhP6151+NLmNhfmF6/ubntfq21UrqnmQrYwAAAMC1pFX/FZuRkaGkpCR17dpVhw4d0t///nc5Ojpq3Lhx8vHx0d13360HH3xQ7du3l7e3t+677z7Fx8erf//+kqSEhARFRkYqOTlZzz33nMrKyvT4448rLS3Ntrt1zz336PXXX9ff/vY3paSk6PPPP9eKFSv00UcfGfmtt1nFJ4u1qGCR1h9Yr1prbaPn9/bvral9purmoJu5dD0AAACuea26kP34448aN26cjh8/ro4dO+r3v/+9vvrqK3Xs2FGS9NJLL8nBwUGjRo1SdXW1hg0bpvnz59vmOzo6au3atZo2bZri4+Pl4eGhiRMn6h//+IdtTEhIiD766CNNnz5dr7zyioKCgrR48WIued/Edh3fpcz8TG0s3fjrg+sR2ylWU6OnKj4wniIGAACANqNVF7Lly5c3eN7NzU3z5s3TvHnzLjuma9euWrduXYP3M3jwYH377bdXlRENyzuap8z8TH158Murmt8/oL+mRE/RjZ1vbOJkAAAAgPFadSHDtclqtWrHkR16I/8NbTu87aruY1DQIKVGpapvp75NnA4AAABoPShkaDJWq1X/OfQfZeZnKvdo7lXdx5DgIZoSPUUR/hFNnA4AAABofShk+M2sVqu++OELZeZnqvB4YaPnO5gcdGu3W5Ualaoefj2aISEAAADQOlHIcNVqamv0WelnyszP1J6Texo938nkpMTuibq7993q5tOt6QMCAAAArRyFDI12ofaCPt7/sRYVLNL+8v2Nnu/s4Kw/9/izUqJSdJ3ndc2QEAAAALg2UMhwxSw1Fq3Zt0aLCxbrx8ofGz3fzdFNd4Tdobt63SWzh7kZEgIAAADXFgoZflV1TbVWFa/SW4VvqexMWaPnt3Nqp7HhY5UcmawO7h2aISEAAABwbaKQ4bKqLFX6155/Keu7LB07e6zR872cvTQ+crzGh4+Xr5tvMyQEAAAArm0UMlyi8nyllhct19vfva2T1ScbPd/X1VcTIidobPhYebl4NUNCAAAAoG2gkMGmvLpcy3Yv07u739Xp86cbPd/fzV+Tek/S6LDRaufcrhkSAgAAAG0LhQw6fva43t71tpb/73JVXahq9HxzO7NSeqfof0L/R25Obs2QEAAAAGibKGR27MiZI8r6Lksr96zUuZpzjZ4f5BmkyVGTdVv32+Ts6NwMCQEAAIC2jUJmhw5VHtJbhW9pVfEqWWotjZ7fzbubpkRP0fCQ4XJy4CkEAAAAXC3+mrYjJRUlWlywWGv3rdUF64VGzw/1C9WU6CkaGjxUjg6OzZAQAAAAsC8UMjuw9+ReLSpYpE8OfKJaa22j5/fy76Wp0VN1c5eb5WByaIaEAAAAgH2ikLVhu4/v1qKCRdpQsuGq5sd0itHU6KkaEDhAJpOpidMBAAAAoJC1Qfn/zVdmfqY2/bjpqub3C+inqdFTFWeOo4gBAAAAzYhC1obsKNuhzPxM5RzOuar5N113k6ZET1HfTn2bOBkAAACA+lDI2oAT505oevZ05R7Nvar5twTfoinRUxTpH9nEyQAAAAA0hELWBvi6+qrifEWj5jiYHDSs2zClRqUq1C+0mZIBAAAAaAiFrA1wMDkoNSpVM76c8atjHU2OSrw+UZOjJqubT7fmDwcAAADgsihkbcSwbsM0f+d8lVSU1Hve2cFZI3uMVErvFAV5BbVwOgAAAAD1oZC1EY4Ojrq799164j9P1Dnu6uiq0WGjNbHXRHX26GxQOgAAAAD1oZC1IYndE7Vw50IdOnNI7k7uGhs+VhMiJ6iDewejowEAAACoB4WsDXF2cFZaTJpKK0p1Z8Sd8nXzNToSAAAAgAZQyNqY27rfZnQEAAAAAFfIwegAAAAAAGCvKGQAAAAAYBAKGQAAAAAYhEIGAAAAAAahkAEAAACAQShkAAAAAGAQChkAAAAAGIRCBgAAAAAGoZABAAAAgEEoZAAAAABgEAoZAAAAABjEyegAbYXVapUkVVRUGJwEFotFVVVVqqiokLOzs9FxYACeA/aN9bdvrL99Y/3tW2ta/4ud4GJHaAiFrImcPn1aktSlSxeDkwAAAABoDU6fPi0fH58Gx5isV1Lb8Ktqa2t16NAheXl5yWQyGR3HrlVUVKhLly764Ycf5O3tbXQcGIDngH1j/e0b62/fWH/71prW32q16vTp0woMDJSDQ8PvEmOHrIk4ODgoKCjI6Bj4GW9vb8P/M8JYPAfsG+tv31h/+8b627fWsv6/tjN2ERf1AAAAAACDUMgAAAAAwCCOs2bNmmV0CKCpOTo6avDgwXJy4lW59orngH1j/e0b62/fWH/7di2uPxf1AAAAAACD8JJFAAAAADAIhQwAAAAADEIhAwAAAACDUMgAAAAAwCAUMrQZc+bM0Y033igvLy916tRJI0eOVFFRkdGxYJBnn31WJpNJ6enpRkdBCzl48KDuvPNO+fv7y93dXVFRUdqxY4fRsdACampqNHPmTIWEhMjd3V3du3fXU089Ja5b1nZt3rxZSUlJCgwMlMlk0gcffFDnvNVq1RNPPKGAgAC5u7tryJAhKi4uNigtmlpD62+xWDRjxgxFRUXJw8NDgYGBmjBhgg4dOmRg4oZRyNBmbNq0SWlpafrqq6+0YcMGWSwWJSQk6MyZM0ZHQwvbvn273njjDUVHRxsdBS3k5MmTGjhwoJydnfXxxx9r165deuGFF+Tn52d0NLSAuXPnasGCBXr99de1e/duzZ07V88995xee+01o6OhmZw5c0Z9+vTRvHnz6j3/3HPP6dVXX9XChQu1bds2eXh4aNiwYTp37lwLJ0VzaGj9q6qqlJubq5kzZyo3N1erVq1SUVGRbrvtNgOSXhkue48267///a86deqkTZs2adCgQUbHQQuprKxUbGys5s+fr6efflp9+/bVyy+/bHQsNLOHH35YW7du1Zdffml0FBggMTFRZrNZb775pu3YqFGj5O7urnfffdfAZGgJJpNJq1ev1siRIyX9tDsWGBiohx56SBkZGZKk8vJymc1mZWVlaezYsUbGRRP75frXZ/v27frd736nkpISBQcHt2C6K8MOGdqs8vJySVL79u0NToKWlJaWphEjRmjIkCFGR0ELWrNmjeLi4jR69Gh16tRJMTExWrRokdGx0EIGDBigjRs3as+ePZKknTt3asuWLRo+fLjByWCE/fv3q6ysrM7vAR8fH/Xr1085OTkGJoNRysvLZTKZ5Ovra3SUel07H2ENNEJtba3S09M1cOBA9e7d2+g4aCHLly9Xbm6utm/fbnQUtLDvv/9eCxYs0IMPPqhHH31U27dv1/333y8XFxdNnDjR6HhoZg8//LAqKioUHh4uR0dH1dTU6JlnntH48eONjgYDlJWVSZLMZnOd42az2XYO9uPcuXOaMWOGxo0bJ29vb6Pj1ItChjYpLS1NhYWF2rJli9FR0EJ++OEHPfDAA9qwYYPc3NyMjoMWVltbq7i4OM2ePVuSFBMTo8LCQi1cuJBCZgdWrFihZcuW6b333lOvXr2Ul5en9PR0BQYGsv6AHbNYLBozZoysVqsWLFhgdJzL4iWLaHPuvfderV27VtnZ2QoKCjI6DlrIN998o6NHjyo2NlZOTk5ycnLSpk2b9Oqrr8rJyUk1NTVGR0QzCggIUGRkZJ1jERERKi0tNSgRWtJf//pXPfzwwxo7dqyioqKUnJys6dOna86cOUZHgwE6d+4sSTpy5Eid40eOHLGdQ9t3sYyVlJRow4YNrXZ3TKKQoQ2xWq269957tXr1an3++ecKCQkxOhJa0C233KKCggLl5eXZbnFxcRo/frzy8vLk6OhodEQ0o4EDB17yMRd79uxR165dDUqEllRVVSUHh7p/0jg6Oqq2ttagRDBSSEiIOnfurI0bN9qOVVRUaNu2bYqPjzcwGVrKxTJWXFyszz77TP7+/kZHahAvWUSbkZaWpvfee08ffvihvLy8bK8T9/Hxkbu7u8Hp0Ny8vLwueb+gh4eH/P39eR+hHZg+fboGDBig2bNna8yYMfr666+VmZmpzMxMo6OhBSQlJemZZ55RcHCwevXqpW+//VYvvviiUlJSjI6GZlJZWam9e/favt6/f7/y8vLUvn17BQcHKz09XU8//bRCQ0MVEhKimTNnKjAwsMEr8eHa0dD6BwQE6I477lBubq7Wrl2rmpoa29+E7du3l4uLi1GxL88KtBGS6r0tWbLE6GgwyM0332x94IEHjI6BFvLvf//b2rt3b6urq6s1PDzcmpmZaXQktJCKigrrAw88YA0ODra6ublZr7/+eutjjz1mra6uNjoamkl2dna9v/MnTpxotVqt1traWuvMmTOtZrPZ6urqar3lllusRUVFxoZGk2lo/ffv33/Zvwmzs7ONjl4vPocMAAAAAAzCe8gAAAAAwCAUMgAAAAAwCIUMAAAAAAxCIQMAAAAAg1DIAAAAAMAgFDIAAAAAMAiFDAAAAAAMQiEDAAAAAINQyAAA15S77rpLI0eONOzxk5OTNXv2bMMe/3IOHDggk8mkvLy8q5q/a9cuBQUF6cyZM02cDADQEAoZAKDVMJlMDd5mzZqlV155RVlZWYbk27lzp9atW6f777/fdmzw4MEymUx69tlnLxk/YsQIW+6m1BylNDIyUv3799eLL77YpPcLAGgYhQwA0GocPnzYdnv55Zfl7e1d51hGRoZ8fHzk6+trSL7XXntNo0ePlqenZ53jXbp0uaQkHjx4UBs3blRAQEALJvxtJk2apAULFujChQtGRwEAu0EhAwC0Gp07d7bdfHx8ZDKZ6hzz9PS8ZHdo8ODBuu+++5Seni4/Pz+ZzWYtWrRIZ86c0aRJk+Tl5aUePXro448/rvNYhYWFGj58uDw9PWU2m5WcnKxjx45dNltNTY1WrlyppKSkS84lJibq2LFj2rp1q+3Y0qVLlZCQoE6dOtUZe/LkSU2YMEF+fn5q166dhg8fruLiYtv5rKws+fr6av369YqIiJCnp6duvfVWHT58WJI0a9YsLV26VB9++KFt5/CLL76wzf/+++/1hz/8Qe3atVOfPn2Uk5NjO1dSUqKkpCT5+fnJw8NDvXr10rp162znhw4dqhMnTmjTpk2X/TkAAJoWhQwAcM1bunSpOnTooK+//lr33Xefpk2bptGjR2vAgAHKzc1VQkKCkpOTVVVVJUk6deqU/vjHPyomJkY7duzQJ598oiNHjmjMmDGXfYz8/HyVl5crLi7uknMuLi4aP368lixZYjuWlZWllJSUS8bedddd2rFjh9asWaOcnBxZrVb96U9/ksVisY2pqqrS888/r3feeUebN29WaWmpMjIyJEkZGRkaM2aMraQdPnxYAwYMsM197LHHlJGRoby8PIWFhWncuHG2Ha+0tDRVV1dr8+bNKigo0Ny5c+vs9rm4uKhv37768ssvr/RHDwD4jShkAIBrXp8+ffT4448rNDRUjzzyiNzc3NShQwelpqYqNDRUTzzxhI4fP678/HxJ0uuvv66YmBjNnj1b4eHhiomJ0VtvvaXs7Gzt2bOn3scoKSmRo6PjJTteF6WkpGjFihU6c+aMNm/erPLyciUmJtYZU1xcrDVr1mjx4sW66aab1KdPHy1btkwHDx7UBx98YBtnsVi0cOFCxcXFKTY2Vvfee682btwoSfL09JS7u7tcXV1tO4cuLi62uRkZGRoxYoTCwsL05JNPqqSkRHv37pUklZaWauDAgYqKitL111+vxMREDRo0qE7GwMBAlZSUNHIFAABXy8noAAAA/FbR0dG2fzs6Osrf319RUVG2Y2azWZJ09OhRST9dnCM7O/uS94JJ0r59+xQWFnbJ8bNnz8rV1VUmk6neDH369FFoaKhWrlyp7OxsJScny8mp7q/Z3bt3y8nJSf369bMd8/f3V8+ePbV7927bsXbt2ql79+62rwMCAmzZf83PfxYX37929OhRhYeH6/7779e0adP06aefasiQIRo1alSd8ZLk7u5u20kEADQ/dsgAANc8Z2fnOl+bTKY6xy6WqNraWklSZWWlkpKSlJeXV+dWXFx8yY7RRR06dFBVVZXOnz9/2RwpKSmaN2+eVq5cWe/LFX/L92O1Whs995ff9+TJk/X9998rOTlZBQUFiouL02uvvVZn/okTJ9SxY8erzg4AaBwKGQDA7sTGxuq7775Tt27d1KNHjzo3Dw+Peuf07dtX0k+f13U5f/nLX1RQUKDevXsrMjLykvMRERG6cOGCtm3bZjt2/PhxFRUV1Tv+clxcXFRTU3PF43+uS5cuuueee7Rq1So99NBDWrRoUZ3zhYWFiomJuar7BgA0HoUMAGB30tLSdOLECY0bN07bt2/Xvn37tH79ek2aNOmyRadjx46KjY3Vli1bLnu/fn5+Onz4sO39Xr8UGhqq22+/XampqdqyZYt27typO++8U9ddd51uv/32K87frVs35efnq6ioSMeOHatzQZCGpKena/369dq/f79yc3OVnZ2tiIgI2/kDBw7o4MGDGjJkyBVnAQD8NhQyAIDdCQwM1NatW1VTU6OEhARFRUUpPT1dvr6+cnC4/K/GyZMna9myZQ3et6+v72V32SRpyZIluuGGG5SYmKj4+HhZrVatW7fukpcpNiQ1NVU9e/ZUXFycOnbsWOdy+w2pqalRWlqaIiIidOuttyosLEzz58+3nX///feVkJCgrl27XnEWAMBvY7Je6YvSAQCwc2fPnlXPnj31z3/+U/Hx8UbHaVLnz59XaGio3nvvPQ0cONDoOABgN9ghAwDgCrm7u+vtt99u8AOkr1WlpaV69NFHKWMA0MLYIQMAAAAAg7BDBgAAAAAGoZABAAAAgEEoZAAAAABgEAoZAAAAABiEQgYAAAAABqGQAQAAAIBBKGQAAAAAYBAKGQAAAAAYhEIGAAAAAAb5Pz1i9or7aKTXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIHXNnsr5u2D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Cu80JFD8Wyi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "b03f3475-5f5c-4501-bd6e-6d31a04b38fa"
      },
      "source": [
        "trace0 = go.Scatter(x=df['Month'], y=df['Monthly Production(bbls)'], mode='lines+markers', name='Actual Data')\n",
        "trace1 = go.Scatter(x=df['Month'], y=q_p, mode='lines+markers', name='Modelled Data')\n",
        "\n",
        "\n",
        "data = [trace0 , trace1]\n",
        "\n",
        "layo = go.Layout(title='Decline Curve Analysis ')\n",
        "# plt.style.use('default')\n",
        "\n",
        "fig = go.Figure(data=data , layout = layo)\n",
        "# iplot(df['Month'], df['Monthly Production(bbls)'] )\n",
        "\n",
        "# iplot(df['Month'], q_p )\n",
        "\n",
        "# iplot(df['Month'], df['Cumm Production(bbls)']/10)\n",
        "\n",
        "iplot(fig)\n",
        "\n",
        "\n",
        "\n",
        "# plt.xlabel('Time (Months)')\n",
        "# plt.ylabel('Monthly Prod. (bbls)')\n",
        "\n",
        "\n",
        "# plt.legend(loc='best')\n",
        "# plt.title('DCA: Model good enough to predict future Q(bbl/month)')\n",
        "# plt.grid()"
      ],
      "execution_count": 57,
      "outputs": [
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}